Artificial neural networks estimate the contribution of taste neurons to coding

Abstract

Taste qualities are believed to be coded in the activity of populations 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 was assessed by constructing simple three-layer neural networks with input neurons that represent 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 four output neurons representing the basic taste qualities, the values of which provide a measure for similarity of test stimuli to the basic taste stimuli. We estimated relative contributions of input neurons 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 jth input neuron to the hidden layer. When the input neurons with a smaller S j (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 was not greatly affected. On the other hand, the taste discrimination of the network progressively deteriorated much more rapidly with pruning of input neurons with a larger S j . These results suggest that cortical taste neurons differentially contribute to the coding of taste qualities. Input neurons with a larger S j tended to be with a larger variation of neural discharge rates to the basic taste stimuli. The variation of neural discharges may be important in the coding of taste qualities.