Theoretical framework and advanced applications of spiking neural networks

Abstract

Developed from traditional Artificial neural networks (ANN), the Spiking neural network (SNN) faithfully mimics the biological behaviours of natural neurons. SNNs transmit information through firing of spiking neurons only when the membrane potential reaches a certain threshold. Because of this property, SNNs are referred to as the most biologically plausible neural model. They are also evaluated as time-efficient and low power-consuming when dealing with complex computational tasks. In this paper, the differences between SNNs and ANNs are first identified. The theoretical framework of the SNN, including the biomedical background, classical spiking neuron models, neural coding mechanisms as well as the learning algorithm are then thoroughly introduced. From the theories, the SNN’s biological plausibility, working principles, strengths and limitations are discussed. Additionally, two applications in the medical & robotics field using the SNN’s pattern recognition and classification are described in detail, indicating its potential in more innovative studies. More imaginative uses of SNNs are in demand for its dominant role in future computational fields.