|InterJournal Complex Systems, 261
|Manuscript Number: |
Submission Date: 981228
|Continuous Learning in Sparsely Connected Hopfield Nets|
Category: Brief Article
Experiments are reported in which sparsely connected Hopfield nets with capped weights perform continuous learning: they are fed an ongoing stream of training patterns, and "remember" the most recent few as attractors. Given a fixed number of neurons, the size of the sliding memory window increases as connectivity increases, so long as the weight cap is chosen appropriately.
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