Efficient biologically-based pattern-recognizing networks

Orly Yadid-Pecht, Moshe Gur

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A biologiclly-motivated classifying neural network which is based on the feature extraction scheme found in the visual cortex is suggested. A special process is proposed for grading and automatically selecting the 'best' features for specific recognition tasks. Ranking is based on a feature's calculated discriminating ability, such that a given class is separated from each and every other class by a given amount. The outcome is a net with less computational complexity than other neural nets, yet one which is more biologically plausible. The main motivation for constructing a reduced net is that the complex circuitry of the brain deals with a huge number of patterns, while a machine-based recognition system usually deals with a limited number of patterns. Results show that feature reduction is drastic and that very compact nets, of the order of tens of neurons, can be used to classify patterns, even in a noisy environment.

Original languageEnglish
Pages (from-to)1061-1070
Number of pages10
JournalNeural Networks
Volume9
Issue number6
DOIs
StatePublished - 1 Jan 1996
Externally publishedYes

Keywords

  • biological vision
  • classifiers
  • feature extraction
  • feature selection
  • neural networks
  • pattern recognition
  • visual cortex

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