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MIT OpenCourseWare

Introduction to Neural Computation

This course introduces quantitative approaches to understanding brain and cognitive functions. Topics include mathematical description of neurons, the response of neurons to sensory stimuli, simple neuronal networks, statistical inference and decision making. It also covers foundational quantitative tools of data analysis in neuroscience: correlation, convolution, spectral analysis, principal components analysis, and mathematical concepts including simple differential equations and linear algebra. (from ocw.mit.edu )

Instructor

Prof. Michale Fee.
Psychology
2020

Record ID

recGwQiEbBC1bDozn

Lecture 01 - Overview and Ionic Currents

Lecture 01 - Overview and Ionic Currents

Lecture 02 - RC Circuit and Nernst Potential

Lecture 03 - Nernst Potential and Integrate and Fire Models

Lecture 04 - Hodgkin-Huxley Model, Part 1

Lecture 05 - Hodgkin-Huxley Model, Part 2

Lecture 06 - Dendrites

Lecture 07 - Synapses

Lecture 08 - Spike Trains

Lecture 09 - Receptive Fields

Lecture 10 - Time Series

Lecture 11 - Spectral Analysis, Part 1

Lecture 12 - Spectral Analysis, Part 2

Lecture 13 - Spectral Analysis, Part 3

Lecture 14 - Rate Models and Perceptrons

Lecture 15 - Matrix Operations

Lecture 16 - Basis Sets

Lecture 17 - Principal Components Analysis

Lecture 18 - Recurrent Networks

Lecture 19 - Neural Integrators

Lecture 20 - Hopfield Networks