Computational Neuroscience > Summary
Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science and psychology with electrical engineering, computer science, mathematics and physics.
Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning, neural networks and computational learning theory 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, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning, neural networks and computational learning theory 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, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
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