Supervised learning in a single layer Dynamic Synapses Neural Network

The main focus of this paper is to introduce a new supervised learning algorithm for spiking neural networks. The learning algorithm minimizes the overall differences between spike times of target and test spike trains by utilizing a new quantitative similarity measure which has been defined in this work. The actual membrane potential of a post-synaptic neuron is adjusted at the time of spikes based on what has been measured from similarity measure in order to generate the desired membrane potential. Finally, by utilizing gradient descent algorithm, the parameters of the spiking neural network are tuned to generate the desired output membrane potential. The proposed algorithm was applied to tune the facilitation, depression, and synaptic weight constants of the Dynamic Synapses Neural Network - DSNN - for the aim of input-output functional mapping. The simulation results show that the system identification task converges to the global optimum. The rate-to-time coding simulation performs with more than 75 percent accuracy. The performance of both system identification and rate-to-time coding is due to adaptation of short and long term synaptic parameters which cannot be accomplished if only synaptic weight is adapted.