Third Generation Neural Networks: Spiking Neural Networks

Abstract

Artificial Neural Networks (ANNs) are based on highly simplified brain dynamics and have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. Throughout their development, ANNs have been evolving towards more powerful and more biologically realistic models. In the last decade, the third generation Spiking Neural Networks (SNNs) have been developed which comprise of spiking neurons. Information transfer in these neurons models the information transfer in biological neurons, i.e., via the precise timing of spikes or a sequence of spikes. Addition of the temporal dimension for information encoding in SNNs yields new insight into the dynamics of the human brain and has the potential to result in compact representations of large neural networks. As such, SNNs have great potential for solving complicated time-dependent pattern recognition problems defined by time series because of their inherent dynamic representation. This article presents an overview of the development of spiking neurons and SNNs within the context of feedforward networks, and provides insight into their potential for becoming the next generation neural networks.