This paper presents a biologically inspired, sequential learning spiking neural classifier (SLSNC) for pattern classification problems. It consists of a two layered neural network and a separate decision block which estimates the predicted class label. Inspired by observations in the neuroscience literature, the input layer employs a new neuron model which converts real valued stimuli into spikes with varying amplitudes and firing times. The intermediate layer neurons are modeled as integrate-and-fire spiking neurons. The decision block identifies that intermediate neuron which fires first and returns the class label associated with that neuron as the predicted class label. The sequential learning algorithm for the spiking neural network automatically determines the network structure from the training samples and adapts its synaptic weights by long term potentiation and long term depression. Performance of SLSNC has been evaluated using a number of benchmark classification problems and the results have been compared with other well-known spiking neural network classifiers in the literature as well as with the standard support vector machine (SVM) with a Gaussian kernel and the fast learning Extreme Learning Machine (ELM) classifiers. The results clearly indicate that the described spiking neural network produces similar or better generalization performance with a smaller network. 
A sequential learning algorithm for a spiking neural classifier