Prototype extraction in material attractor neural networks with stochastic dynamic learning

Stefano Fusi

doi:10.1117/12.205099

6 April 1995 Prototype extraction in material attractor neural networks with stochastic dynamic learning

Stefano Fusi

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205099
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Stefano Fusi "Prototype extraction in material attractor neural networks with stochastic dynamic learning", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205099

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