PRUNING OF RAT CORTICAL TASTE NEURONS BY AN ARTIFICIAL NEURAL-NETWORKMODEL

1. Taste qualities are believed to be coded in the activity of ensembl es of taste neurons. However, it is not clear whether all neurons are equally responsible for coding. To clarify the point, the relative con tribution of each taste neuron to coding needs to be assessed. 2. We c onstructed simple three-layer neural networks with input units represe nting cortical taste neurons of the rat. The networks were trained by the back-propagation learning algorithm to classify the neural respons e patterns to the basic taste stimuli (sucrose, HCl, quinine hydrochlo ride, and NaCl). The networks had four output units representing the b asic taste qualities, the values of which provide a measure for simila rity of test stimuli (salts, tartaric acid, and umami substances) to t he basic taste stimuli. 3. Trained networks discriminated the response patterns to the test stimuli in a plausible manner in light of previo us physiological and psychological experiments. Profiles of output val ues of the networks paralleled those of across-neuron correlations wit h respect to the highest or second-highest values in the profiles. 4. We evaluated relative contributions of input units to the taste discri mination of the network by examining their significance S-j, which is defined as the sum of the absolute values of the connection weights fr om the jth input unit to the hidden layer. When the input units with w eaker connection weights (e.g., 15 of 39 input units) were ''pruned'' from the trained network, the ability of the network to discriminate t he 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 uni ts with stronger connection weights. 5. These results suggest that cor tical taste neurons differentially contribute to the coding of taste q ualities. The pruning technique may enable the evaluation of a given t aste neuron in terms of its relative contribution to the coding, with S-j providing a quantitative measure for such evaluation.