Optimization of Spiking Neural Networks with dynamic synapses for spike sequence generation using PSO

We present a method that is based on Particle Swarm Optimization (PSO) for training a Spiking Neural Network (SNN) with dynamic synapses to generate precise time spike sequences. The similarity between the desired spike sequence and the actual output sequence is measured by a simple leaky integrate and fire spiking neuron. This measurement is used as a fitness function for PSO algorithm to tune the dynamic synapses until a desired spike output sequence is obtained when certain input spike sequence is presented. Simulations are made to illustrate the performance of the proposed method.