A novel method to analyse response patterns of taste neurons by artificial neural networks

Takatoshi Nagai, Takashi Yamamoto, Hiroshi Katayama, Masaharu Adachi, Kazuyuki Aihara

研究成果: Article査読

8 被引用数 (Scopus)

抄録

Despite several notions on the gustatory code proposed over three decades, investigators have not yet reached a consensus. This paper describes a new approach to analyse gustatory neural activities. Three-layer neural networks were trained by the back-propagation learning algorithm, to classify the neural response patterns to four basic taste qualities. The discrimination by the trained networks on taste qualities in the response patterns of rat chorda tympani fibres (CT) and cortical taste neurons (CN) was consistent both with the correlation analysis and with behavioural experiments. By examining the connection weights of each neuron, some input neurons representing CN were 'pruned' without deteriorating the ability of the network to discriminate taste. This characteristic of the network is contrary to a previous hypothesis, that taste neurons are of equal importance in the neural coding.

本文言語English
ページ(範囲)745-748
ページ数4
ジャーナルNeuroReport
3
9
DOI
出版ステータスPublished - 1992 9月

ASJC Scopus subject areas

  • 神経科学(全般)

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