Comparative Study between Multiplicative Neuron and Spiking Neuron Model

In the present research article, single Multiplicative Neuron Model (MNM) is compared with Single Spiking Neuron Model (SNM) using back propagation learning algorithm. The authors employed IRIS and Breast Cancer Wisconsin datasets for training and testing these models. Simulation results reveal that for both the datasets, MNM model is capable to perform classification task in less iteration than SNM model i.e. the former gets trained more quickly. On the contrary, the classification performance in terms of various parameters of an artificial neural network of MNM model found to be poorer than that of SNM model for both the datasets being studied. Furthermore, the results also show that miss-classification rate is higher in MNM model.