Pruning of rat cortical taste neurons by an artificial neural network
model.
Nagai, Takatoshi, Hiroshi Katayama, Kazuyuki Aihara, and Takashi Yamamoto.
Department of Physiology, Teikyo University School of Medicine, Tokyo 173;
Department of Electronic Engineering, Faculty of Engineering, Tokyo Denki
University, Tokyo 101; Department of Mathematical Engineering and Information
Physics, Faculty of Engineering, The University of Tokyo, Tokyo 113;
Department of Behavioral Physiology, Faculty of Human Sciences, Osaka
University, Osaka 565, Japan.
APStracts 2:0145N, 1995.
SUMMARY AND CONCLUSIONS
1. Taste qualities are believed to be coded in the activity of ensembles of
taste neurons. However, it is not clear whether all neurons are equally
responsible for coding. To clarify the point the relative contribution of each
taste neuron to coding needs to be assessed. 2. We constructed simple three-
layer neural networks with input units representing cortical taste neurons of
the rat. The networks were trained by the back-propagation learning algorithm
to classify the neural response patterns to the basic taste stimuli (sucrose,
HCl, quinine-hydrochloride and NaCl). The networks had 4 output units
representing the basic taste qualities, the values of which provide a measure
for similarity of test stimuli (salts, tartaric acid and umami substances) to
the basic taste stimuli. 3. Trained networks discriminated the response
patterns to the test stimuli in a plausible manner in light of previous
physiological and psychological experiments. Profiles of output values of the
networks paralleled those of across-neuron correlations with respect to the
highest or second highest values in the profiles. 4. We evaluated relative
contributions of input units to the taste discrimination of the network by
examining their significance S j , which is defined as the sum of the absolute
values of the connection weights from the j th input unit to the hidden layer.
When the input units with weaker connection weights (e.g., 15 out of 39 input
units) were "pruned" from the trained network, the ability of the network to
discriminate the basic taste qualities as well as other test stimuli was not
greatly affected. On the other hand, the taste discrimination of the network
progressively deteriorated much more rapidly with pruning of input units with
stronger connection weights. 5. These results suggest that cortical taste
neurons differentially contribute to the coding of taste qualities. The
pruning technique may enable the evaluation of a given taste neuron in terms
of its relative contribution to the coding, with significance S j providing a
quantitative measure for such evaluation.
Received 31 October 1994; accepted in final form 25 April 1995.
APS Manuscript Number J684-4.
Article publication pending J. Neurophysiol.
ISSN 1080-4757 Copyright 1995 The American Physiological Society.
Published in APStracts on 16 May 1995.