A Fast Precise-Spike and Weight-Comparison Based Learning Approach for Evolving Spiking Neural Networks

摘要

Evolving spiking neural networks (ESNNs) evolve the output neurons dynamically based on the information presented in the incoming samples and the information stored in the network. In order to improve the learning efficiency of the existing algorithms for ESNNs, this paper presents a fast precise-spike and weight-comparison based learning algorithm, called PSWC. PSWC can dynamically add a new neuron or update the parameters of existing neurons according to the precise time of the incoming spikes and the similarities of the weights. The proposed algorithm is demonstrated on several standard data sets. The experimental results demonstrate that PSWC has a significant advantage in terms of speed performance and provides competitive results in classification accuracy compared with SpikeTemp and rank-order-based approach.