Artificial neural network analysis of gustatory responses in the thalamic taste relay of the rat.

We used an artificial neural network (ANN) as a model for analyzing single-neuron responses from the thalamic taste relay of rats. The network consisted of: (1) a layer of 44 input units, representing the responses of the 44 thalamic taste cells; (2) a layer of hidden units of varying numbers; and (3) a layer of four output units. We used the back-propagation algorithm to train the output units to discriminate among tastants based on inputs from the thalamic neurons. As the network became fully trained, we found that: (1) only two hidden units were necessary to provide nearly the full discriminative capacity of the network; (2) the loss of even a few of the input units that had the highest impact on hidden units caused a drastic reduction of discriminative power, implying that not all neurons contribute equally to the neural code; and (3) adding a temporal component to the input, by representing each 100-ms time bin as a separate input unit, increased the accuracy with which output units were able to identify tastants. We used data from behavioral discrimination tasks as a measure of the capacity of the network to identify stimuli correctly. A network with two hidden units was about as effective as an across-pattern analysis in accounting for the rat's discriminative ability.