Temporal coding in realistic neural networks

The modification of a realistic neural network model is proposed. The model differs from the Hopfield model because of the two characteristic contributions to synaptic efficacies: the short-time contribution which is determined by the chemical reactions in the synapses, and the long-time contribution corresponding to the structural changes of synaptic contacts. The approximation solution of the realistic neural network model equations is obtained. This solution allows us to calculate the postsynaptic potential as the function of input. Using the approximate solution of realistic neural network model equations the behaviour of postsynaptic potential of a realistic neural network as a function of time for different temporal sequences of stimuli is described. Various outputs are obtained for different temporal sequences of the given stimuli. These properties of temporal coding can be exploited as a recognition element capable of being selectively tuned to different inputs