Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

March 1993, Vol. 5, No. 2, Pages 242-259
(doi: 10.1162/neco.1993.5.2.242)
© 1993 Massachusetts Institute of Technology
Learning Competition and Cooperation
Article PDF (1.01 MB)
Abstract

Competitive activation mechanisms introduce competitive or inhibitory interactions between units through functional mechanisms instead of inhibitory connections. A unit receives input from another unit proportional to its own activation as well as to that of the sending unit and the connection strength between the two. This, plus the finite output from any unit, induces competition among units that receive activation from the same unit. Here we present a backpropagation learning rule for use with competitive activation mechanisms and show empirically how this learning rule successfully trains networks to perform an exclusive-OR task and a diagnosis task. In particular, networks trained by this learning rule are found to outperform standard backpropagation networks with novel patterns in the diagnosis problem. The ability of competitive networks to bring about context-sensitive competition and cooperation among a set of units proved to be crucial in diagnosing multiple disorders.