A New Supervised Spiking Neural Network

A more computational spiking neural network, PTSNN, was proposed. In PTSNN, the synaptic connection weights between neurons were set to one. Network runs through modulating the PSP location in timeline of each neuron by adapting their accepted time make the network spike at the right time so that meet the requirement of classification. The weight modulating of PTSNN is determined by the error of actual spike time and expectation time as thus avoid calculating the derivative of error function which is often used in other SNNs. The PTSNN has more computational advantage. We perform experiments for the classical Iris dataset problem with less neurons compare to other neuron networks and the results show that it is capable to classify data set on non-linearly problem with convergence accuracy comparable to traditional sigmoidal network and other spiking neural networks. The proposed network is promise in classification problems.