Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411096 | Neurocomputing | 2009 | 11 Pages |
Abstract
We investigate sparse networks of threshold units, trained with the perceptron learning rule to act as associative memories. The units have position and are placed in a ring so that the wiring cost is a meaningful measure. A genetic algorithm is used to evolve networks that have efficient wiring, but also good functionality. It is shown that this is possible, and that the connection strategy used by the networks appears to maintain some connectivity at all distances, but with the probability of a connection decreasing rapidly with distance.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Rod Adams, Lee Calcraft, Neil Davey,