Simulation of Biological Learning with Spiking Neural Networks

Abstract

Artificial neural network (ANN) research is inspired by how information is dynamically and massively processed in parallel by biological neural networks. Spiking neural networks (SNNs) are versions of artificial neural networks that are more biologically realistic than commonly used static models. As in actual brains, neurons signal each other through current spikes (rather than constant inputs, as in conventional ANNs), and spike timing plays a key role in SNN functioning. In this work, we give an overview of SNNs, and we describe and implement three different mathematical models (integrate and fire, leaky integrate and fire, conductance-based) using the Brian2 software simulator. We also describe the training of SNNs using the spike-timing-dependent plasticity (STDP) algorithm, and discuss an experiment by Diehl and Cook that shows the ability of SNNs to learn to distinguish handwritten digits. Lastly, we describe a few available software SNN simulators and hardware SNN implementations with possible practical applications of SNNs.