Theoretical Neuroscience

Computational neuroscience is an approach to understanding the information content of neural signals by modeling the nervous system at many different structural scales , including the biophysical, the circus, and the system levels.

Theoretical analysis and Computational modeling are important tools determining how they function, why they operate in particular ways. Neuroscience encompasses approaches ranging from molecular and cellular studies to human psychophysic and psychology.

The questions what,how,and why are addressed by descriptive mechanistic , and interpretive models. Descriptive models summarize large amounts of experimental data compactly yet accurately, thereby characterizing what neurons and neural circuits do.

Mechanistic models address the question of how nervous system operate on basic of known anatomy ,psychology, and circuitry.

Interpretive models models use computational and information-theoretic principles to explore the behavioral and cognitive significance of the various aspects of nervous system function , addressing the question of why nervous systems operate as they do.

A frequent mistake is to assume that a more detailed model is necessary superior. Models must be detailed enough to make contact with the lower level yet simple enough to provide clear results at the higher level.

Theoretical neuroscience is based on the belief of that methods of mathematics , physics and computer science can provide important insights into nervous system function.