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Proceedings Paper

Habituation-based mechanism for encoding temporal information in artificial neural networks
Author(s): Bryan W. Stiles; Joydeep Ghosh
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Paper Abstract

A novel neural network is proposed for the dynamic classification of spatio-temporal signals. The network is designed to classify signals of different durations, taking into account correlations among different signal segments. Such a network is applicable to SONAR and speech signal classification problems, among others. Network parameters are adapted based on the biologically observed habituation mechanism. This allows the storage of contextual information, without a substantial increase in network complexity. Experiments on classification of high dimensional feature vectors obtained from Banzhaf sonograms, demonstrate that the proposed network performs better than time delay neural networks while using a less complex structure. A mathematical justification of the capabilities of the habituation based mechanism is also provided.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205146
Show Author Affiliations
Bryan W. Stiles, Univ. of Texas/Austin (United States)
Joydeep Ghosh, Univ. of Texas/Austin (United States)


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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