A Parallel Combination of Facilitating Synapse Based on Temporal Correlation in SpikeProp Algorithm

This paper presents a new method to minimize the error function between the expected spike time and the actual spike time, which is a parallel combination of facilitating synapses consisting of an excitatory and an inhibitory synapse. The SpikeProp algorithm is designed to solve the error optimization problem between the expected spike time and the actual spike time of the current from the presynaptic neuron passing through the synapse to the postsynaptic neuron. The SpikeProp algorithm merges the Bienenstock–Cooper–Munro (BCM) rule with Spike Timing Dependent Plasticity (STDP) before calculating errors. The idea of filtration based on value in Synaptic Weight Association Training (SWAT) is utilized in the hidden layer. Thus, a time selector is used in the synapse between the input layer and the hidden layer, which is achieved through parallel combination of excitatory and inhibitory synapses. The neuron models used in these two processes are Leaky Integrate and Fired (LIF) and Spike Response Model (SRM), respectively. The algorithm is benchmarked against the nonlinear exclusive OR (XOR) problem. The simulation results has illustrated the diagram of the time selector in the hidden layer and the error measured in the output layer.