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Proceedings Paper

Neurocomputer Nearest Matched Filter Classification Of Spatiotemporal Patterns
Author(s): Robert Hecht-Nielsen
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Paper Abstract

Recent advances in massively parallel optical and electronic neural network processing technology have made it plausible to consider the use of matched filter banks containing large numbers of individual filters as pattern classifiers for complex spatiotemporal pattern environments such as speech, sonar, radar, and advanced communications. This paper begins with all overview of how neural networks can be used to approximately implement such multidimensional matched filter banks. The "nearest matched filter" classifier is then formally defined. It is then noted that, given a statistically comprehensive set of filter templates, the nearest matched filter classifier will have near-Bayesian performance for spatiotemporal patterns. The combination of near-Bayesian classifier performance with the excellent performance of matched filtering in noise yields a powerful new classification technique. This adds additional interest to Grossberg's hypothesis that the mammalian cerebral cortex carries out local-in-time nearest matched filter classification of both auditory and visual sensory inputs as an initial step in sensory pattern recognition - which may help explain the almost instantaneous pattern recognition capabilities of animals.

Paper Details

Date Published: 11 August 1987
PDF: 11 pages
Proc. SPIE 0752, Digital Optical Computing, (11 August 1987); doi: 10.1117/12.939915
Show Author Affiliations
Robert Hecht-Nielsen, Hecht-Nielsen Neurocomputer Corporation (United States)

Published in SPIE Proceedings Vol. 0752:
Digital Optical Computing
Raymond Arrathoon, Editor(s)

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