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Introduction to Computational Neuroscience, Spring 2002

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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.

Subject:: Science and Technology
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

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Top Keywords

8.261J 9.29J Cable Theory Convolution Correlation Dynamics Fourier Analysis Information Theory Ion Channels Linear Systems Neural Coding Neural Excitability probability theory Signal Detection Theory Stochastic Models

Conditions of Use

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Read the Fine Print

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Your redistributing comes with some restrictions. Do not remix or make derivative works.

Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.

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