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Proceedings Paper

Prototype extraction in material attractor neural networks with stochastic dynamic learning
Author(s): Stefano Fusi
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Paper Abstract

Dynamic learning of random stimuli can be described as a random walk among the stable synaptic values. It is shown that prototype extraction can take place in material attractor neural networks when the stimuli are correlated and hierarchically organized. The network learns a set of attractors representing the prototypes in a completely unsupervised fashion and is able to modify its attractors when the input statistics change. Learning and forgetting rates are computed.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205099
Show Author Affiliations
Stefano Fusi, INFN-Istituto Nazionale di Fisica Nucleare (Italy)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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