Computational neuroscience | Wikipedia audio article
Channel: wikipedia tts
Date: 2018-12-25

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Computational_neuroscience


00:01:33 1 History
00:03:36 2 Major topics
00:04:02 2.1 Single-neuron modeling
00:06:23 2.2 Development, axonal patterning, and guidance
00:07:25 2.3 Sensory processing
00:08:21 2.4 Memory and synaptic plasticity
00:09:58 2.5 Behaviors of networks
00:11:45 2.6 Cognition, discrimination, and learning
00:13:21 2.7 Consciousness
00:13:49 2.8 Computational clinical neuroscience
00:14:21 3 See also
00:15:16 4 Notes and references
00:15:25 5 Bibliography
00:18:53 6 External links
00:19:02 6.1 Journals
00:19:30 6.2 Software
00:19:56 6.3 Conferences
00:21:05 6.4 Websites



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SUMMARY
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Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which 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.Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not concerned with biologically unrealistic disciplines such as connectionism, machine learning, artificial neural networks, artificial intelligence and computational learning theory.In theory, computational neuroscience would be a sub-field of theoretical neuroscience which employs computational simulations to validate and solve the mathematical models. However, since the biologically plausible mathematical models formulated in neuroscience are in most cases too complex to be solved analytically, the two terms are essentially synonyms and are used interchangeably. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field.The mathematical models formulated in computational neuroscience are useful since they 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. Furthermore, these computational models frame hypotheses that can be directly tested by biological or psychological experiments.