Brief History of Neural Networks

Neural Networks Module

The module below can be covered in a three week period in the Introduction to AI Course

Brief History of Neural Networks
Definition of a Neural Network
Learning in a Neural Network
The Pattern Associator
The Hebb Rule
The Delta Rule
The Generalized Delta Rule

This module on Neural Networks was written by Ingrid Russell of the University of Hartford . It is being printed with permission from Collegiate Microcomputer Journal.

If you have any comments or suggestions, please send email to irussell@mail.hartford.edu

Brief History of Neural Networks

The earliest work in neural computing goes back to the 1940's when McCulloch and Pitts introduced the first neural network computing model. In the 1950's, Rosenblatt's work resulted in a two-layer network, the perceptron, which was capable of learning certain classifications by adjusting connection weights. Although the perceptron was successful in classifying certain patterns, it had a number of limitations. The perceptron was not able to solve the classic XOR (exclusive or) problem. Such limitations led to the decline of the field of neural networks. However, the perceptron had laid foundations for later work in neural computing.

In the early 1980's, researchers showed renewed interest in neural networks. Recent work includes Boltzmann machines, Hopfield nets, competitive learning models, multilayer networks, and adaptive resonance theory models.