Please see the CISC 452 / COGS 400 course website for up-to-date information.

CISC-452/3.0 (COGS-400/3.0) Neural and Genetic Computing

Original author: Roger Browse
Last Revised: November 06, 2013

Calendar Description

Introduction to neural and genetic computing. Topics include associative memory systems, neural optimization strategies, supervised and unsupervised classification networks, genetic algorithms, genetic and evolutionary programming. Applications are examined, and the relation to biologic systems is discussed.

Learning Hours: 120 (36L;84P)

Prerequisite CISC 235/3.0.

Exclusion: COGS 400/3.0


Since the first appearance of digital computers, the dominant theory of biologic brain operation has centred on the notion that neurons transmit and receive information signals. Over the past sixty years, this notion has been elaborated in computational neural network systems which have powerful problem solving capabilities. The objective of this course is to introduce the most commonly used neural and genetic computational processes, along with an understanding of the types of problems for which such systems are most useful. Throughout the course, attention is paid to the consistencies with biologic processes. Within the course, students gain practical experience in designing and implementing typical neural and genetic solutions. Students whose primary interest is in the biologic sciences, or in psychology will gain exposure to computational approaches which are commonly used in those fields.


Biologic Brain Function
Basic Feedforward Networks: Perceptron, Backpropagation
Models of Associative Memory
Hopfield Network and Boltzmann Machine
Unsupervised Learning Systems
Genetic Algorithms and Genetic Programming
Principal Components Analysis Networks
Radial Basis Function Networks
Modular Networks: Counterpropagation, Neocognitron

Possible Texts

  • Elements of Artificial Neural Networks, by Mehrotra, Mohan, & Ranka, The MIT Press, 1997.

  • An Introduction to Neural Networks, by James A. Anderson, The MIT Press, 1995.

  • Neural Networks: A Comprehensive Foundation, Simon Haykin, Prentice-Hall, 2nd edition, 1998.