Encoding and classification in a model of olfactory cortex

Todd Leen, Max Webb, Steve Rehfuss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is observed that the computational model of the olfactory cortex given by J. Ambros-Ingerson et al. (1990) is closely related to multistage vector quantization. Variations of the architecture and learning rules are given. The authors evaluate the performance of the various models applied to encode and classify vowels extracted from spoken letters. The efficacy of neural implementation of multistage and tree-search quantization is demonstrated. For fixed branching ratio it is seen that the tree-search quantizer consistently outperforms the multistage structure, though at considerable resource cost. For networks with equal neural resources, the multistage architecture returns significantly lower MSE than the flat and tree-search architectures. Experiments show that pattern rescaling offers a degree of noise immunity.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Pages553-559
Number of pages7
ISBN (Print)0780301641
StatePublished - Jan 1 1992
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Publication series

NameProceedings. IJCNN - International Joint Conference on Neural Networks

Other

OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period7/8/917/12/91

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Leen, T., Webb, M., & Rehfuss, S. (1992). Encoding and classification in a model of olfactory cortex. In Anon (Ed.), Proceedings. IJCNN - International Joint Conference on Neural Networks (pp. 553-559). (Proceedings. IJCNN - International Joint Conference on Neural Networks). Publ by IEEE.