9.641J / 8.594J Introduction to Neural Networks, Fall 2002

Seung, H. Sebastian

Author(s)

Seung, H. Sebastian

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Alternative title

Introduction to Neural Networks

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Abstract

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development. Alternate years.

Date issued

2002-12

URI

http://hdl.handle.net/1721.1/36327

Department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Department of Physics

Other identifiers

9.641J-Fall2002

local: 9.641J

local: 8.594J

local: IMSCP-MD5-bd6878f7d57a228449535604255a526a

Keywords

synaptic connectivity, computation, learning, multilayer perceptrons, recurrent networks, amplifiers, attractors, hybrid computation, Backpropagation, Hebbian learning, perception, motor control, memory, neural development, 9.641J, 8.594J, 9.641, 8.594, Neural networks (Computer science)

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MIT OCW Archived Courses