Computational neuroscience

Computational neuroscience is a branch of neuroscience that employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system.

Overview[edit | edit source]

Computational neuroscience is distinct from psychological computational models and purely phenomenological computational models, in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, proteins, and chemical coupling to network oscillations, columnar and topographic architecture, and learning and memory.

History[edit | edit source]

The field of computational neuroscience was pioneered by Wilfrid Rall, Hodgkin and Huxley, who used mathematical analysis in combination with electrophysiological experiments to infer how neurons process signals. These investigators and others, such as Jack Cowan, Hugh Wilson, and Shigeru Amari, used these methods to show how neural systems could compute a wide range of computational functions.

Methods[edit | edit source]

Computational neuroscience methods are divided into two categories: bottom-up and top-down. Bottom-up methods are those that try to build up models of brain function from an understanding of the biophysical properties of neurons, while top-down methods seek to understand brain function in terms of the brain's high-level computational abilities, and to build models that can perform similar tasks.

Applications[edit | edit source]

Computational neuroscience has many practical applications. For example, it can be used to develop artificial intelligence systems, to help understand and treat neurological disorders, and to design neural prosthetics.

References[edit | edit source]

External links[edit | edit source]

Computational neuroscience Resources
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