Introduction to Computational Neuroscience, Spring 2002

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Author:

Seung, Sebastian

Subject:

Science and Technology

Institution Name:

M.I.T.

Collection:

MIT OpenCourseWare

Grade Level:

Post-secondary

Abstract:

Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

Languages:

English

Material Type:

Full Course, Homework and Assignments, Lecture Notes, Syllabi

Media Format:

Text/HTML, Downloadable docs

Conditions of Use:

Creative Commons Attribution-Noncommercial-Share Alike 2.5