A basis coupled evolving spiking neural network with afferent input neurons

This paper presents an evolving spiking neural network namely, `Basis Coupled Evolving Spiking Neural Network (BCESNN)´ and its learning algorithm to solve real-valued pattern recognition problems. BCESNN is a two-layered neuron model with afferent neurons in the input layer and efferent neurons in the output layer. The afferent neurons in the input layer convert the real-valued input feature to a train of spikes using a bank of Gaussian Receptive Field (GRF) for each individual feature. The number of GRF per feature is fixed a priori. Each efferent neuron in the output layer is associated to a class. Efferent neurons are integrate-and-fire type neuron. BCESNN has an evolving architecture that uses basis coupled rank order learning (BCROL) algorithm to estimate the number of output neurons and the network parameters. Each sample is presented only once to the network. When a new sample is presented to the network either we add a neuron or we update an existing neuron. Weight estimation for added neuron is done using BCROL and weight update is done using Euclidean distance based distance measure. In the performance section we conducted three different experiments. Firstly we compared the performance of BCROL against Rank Order Learning(ROL). Next, we evaluated the performance of BCESNN on benchmark classification problems from the UCI machine learning repository. Finally, we evaluated the performance of a sparsely connected BCESNN against fully connected BCESNN where connectivity refers to the number of GRF connected to the afferent neurons.