9.40 Introduction to Neural Computation (Spring 2018, MIT OCW): Lecture 20

9.40 Introduction to Neural Computation

9.40 Introduction to Neural Computation (Spring 2018, MIT OCW). Instructor: Prof. Michale Fee. 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)

Lecture 20 - Hopfield Networks

This video covers recurrent networks with lambda greater than one, attractor networks for long-term memory, winner-take-all networks, and Hopfield network capacity.

Go to the Course Home or watch other lectures:

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