A novel method to analyse response patterns of taste neurons by artificial neural networks.
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.
Nagai
T
T
Department of Physiology, Teikyo University School of Medicine, Tokyo, Japan.
Yamamoto
T
T
Katayama
H
H
Adachi
M
M
Aihara
K
K
D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology*; D002814:Chorda Tympani Nerve / Q000166:cytology / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D051381:Rats; D013649:Taste / Q000502:physiology*