9.641J | Spring 2005 | Graduate

Introduction to Neural Networks

Course Description

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models …
This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.
Learning Resource Types
Lecture Notes
Problem Sets
Neurons forming a network in disassociated cell culture.
Neurons forming a network in disassociated cell culture. (Image courtesy of Seung Laboratory, MIT Department of Brain and Cognitive Sciences.)