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Browse materialsMIT 9.641J · Spring 2005

Introduction to Neural Networks

Subjects: Physics · Science

Art: User:Looie496 created file, Santiago Ramon y…, Cajal cortex drawings, 2009 · Public domain

Instructor: Prof. Sebastian Seung
Term: Spring 2005
Level: Graduate
What's inside: 1 lecture note · 1 reading · 30 other files · 4 web pages
Materials open: MIT OCW

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Source: MIT OpenCourseWare 9.641J Introduction to Neural Networks, Spring 2005, Prof. Sebastian Seung — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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Lecture Notes

13 files

Assignments

16 problem sets

Other materials from this course

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MIT OpenCourseWare, Prof. Sebastian Seung, 9.641J Introduction to Neural Networks, Spring 2005.

License: CC BY-NC-SA 4.0 · Edition: Spring 2005 · Open the original course on OCW ↗

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Introduction to Neural Networks

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Assignments

Other materials from this course

Lecture 1: From Spikes to Rates

Lecture 2: Perceptrons: Simple and Multilayer

Lecture 3: Perceptrons as Models of Vision

Lecture 4: Linear Networks

Lecture 5: Retina

Lecture 6: Lateral Inhibition and Feature Selectivity

Lecture 7: Objectives and Optimization

Lecture 8: Hybrid Analog-Digital Computation Ring Network

Lecture 9: Constraint Satisfaction Stereopsis

Lecture 10: Bidirectional Perception

Lecture 11: Signal Reconstruction

Lecture 12: Hamiltonian Dynamics

Lecture 13: Antisymmetric Networks

Lecture 14: Excitatory-Inhibitory Networks Learning

Lecture 15: Associative Memory

Lecture 16: Models of Delay Activity Integrators

Lecture 17: Multistability Clustering

Lecture 18: VQ PCA

Lecture 19: More PCA Delta Rule

Lecture 20: Conditioning Backpropagation

Lecture 21: More Backpropagation

Lecture 22: Stochastic Gradient Descent

Lecture 23: Reinforcement Learning

Lecture 24: More Reinforcement Learning

Lecture 25: Final Review