Study on time-length processing by neural networks

Shin'ichiro Kanoh, Ryoko Futami, Nozomu Hoshimiya

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

1 Citation (Scopus)

Abstract

It is thought that 'time-length', which is one of the most important parameters for representing temporal patterns, is coded on some way in the brain. By two methods, we tried to estimate the principle of coding/memorizing time-length, focusing on 'the variation of the encoded time-length image on time'. One was auditory psychophysical experiment on time-length comparisons using the Constant Method. We found that the tendency of the interval of uncertainty, which indicates an amount of forgetting, depended both on first tone length and on interval of two tones. And the other was to evaluate the principle with which recurrent neural networks came to compare two time-length patterns after learning. In these simulations, it was estimated that the time-length information tended to be coded into the position on the trajectories toward the attractors in state space. It was also shown that this network revealed some characteristics of human memory decay when it was fluctuated by random noise.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages151-154
Number of pages4
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1993 Dec 1
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume1

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
CityNagoya, Jpn
Period93/10/2593/10/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Kanoh, S., Futami, R., & Hoshimiya, N. (1993). Study on time-length processing by neural networks. In Proceedings of the International Joint Conference on Neural Networks (pp. 151-154). (Proceedings of the International Joint Conference on Neural Networks; Vol. 1). Publ by IEEE.