Spiking Neuron Model with Gamma-distributed Synaptic Weights for Different Thresholds

Abstract

In an attempt to propose a closer model of a biological neuron, various artificial neural models have been reported in the literature. Very few reported articles are available which consider the time-varying synaptic weights of the model. Hence there is further scope to develop and investigate alternative improved spiking neural models which will better represent the activities of a biological neuron. With this motivation, the synaptic weight of the conventional integrate and fire (CIF) model is considered as gamma distributed time-varying nature. Further, for spike generation at the output of the model, different thresholds are employed. The gamma distribution in weight is assumed to take into account the temporal behavior of the synapse. To assess the performance of the proposed model, statistical properties such as similarity indices of the output sequence, mean and variance of normalized similarity indices (NSI) are obtained from simulation-based experiments and are compared.