A sequential learning algorithm for a Minimal Spiking Neural Network (MSNN) classifier

In this paper, we develop a new sequential learning algorithm for a spiking neural network classifier. The algorithm handles the input features that are not in the form of a spike train but in a real-valued (analog) form. The sequential learning algorithm evolves the number of spiking neuron automatically based on the information present in the current sample and results in a compact architecture. Hence, it is referred to as a Minimal Spiking Neural Network (MSNN). The learning algorithm can either add a new neuron to the network or update the parameters of the existing neurons based on the information contained in the arriving samples. The update rule uses excitatory/inhibitatory rule to capture the knowledge contained in the current sample. Performance evaluation of the proposed MSNN is presented using two benchmark problems from the UCI machine learning repository, namely, the Iris flower classification and Wisconsin breast cancer problem and the results are compared with other existing spiking neural algorithms like SpikeProp, MuSpiNN and Multi-spike learning algorithms. The results clearly indicate the better performance of MSNN with a compact architecture.