The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.
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