Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
2077164 | Biosystems | 2007 | 7 Pages |
Abstract
We investigate an artificial neural network model with a modified Hebb rule. It is an auto-associative neural network similar to the Hopfield model and to the Willshaw model. It has properties of both of these models. Another property is that the patterns are sparsely coded and are stored in cycles of synchronous neural activities. The cycles of activity for some ranges of parameter increase the capacity of the model. We discuss basic properties of the model and some of the implementation issues, namely optimizing of the algorithms. We describe the modification of the Hebb learning rule, the learning algorithm, the generation of patterns, decomposition of patterns into cycles and pattern recall.
Related Topics
Physical Sciences and Engineering
Mathematics
Modelling and Simulation
Authors
Julius Stroffek, Eduard Kuriscak, Petr Marsalek,