A two stage learning algorithm for a Growing-Pruning Spiking Neural Network for pattern classification problems

This paper presents a two stage learning algorithm for a Growing-Pruning Spiking Neural Network (GPSNN) for pattern classification problems. The GPSNN uses three layered network architecture with input layer employing a modified population coding and, leaky integrate-and-fire spiking neurons in the hidden and output layers. The class label for a sample is determined according to the output neuron with minimum spike latency. The learning algorithm for the GPSNN employs a two stage learning mechanism. In the first stage, the hidden layer is grown and adapted to map the inputs to a hyperdimensional space. In the second stage, the hidden layer neurons with low dominance are pruned and the response of the most dominant neurons is mapped to the output space. The proposed approach has been evaluated on benchmark data sets from the UCI machine learning repository and the results were compared with batch as well as online spiking neural networks. The results clearly highlight that the GPSNN can achieve better performances using a compact network structure.