Function approximation by hardware spiking neural network

Spiking neural networks (SNN) represent a special class of artificial neural networks, where neu-ron models communicate by sequences of spikes. SNNs are often referred to as the third generation of neural networks that highly inspired from natural computing in the brain and recent advances in neuroscience. In this paper we implement biologically-inspired, hardware-realizable SNN architecture using integrate-and-fire units, which is capable of approximating a real-valued function. Based on the results of MATLAB simulations, hardware synthesis and FPGA implementation, it is demonstrated that the implemented hardware can approximate linear and nonlinear functions with low minimum relative error. This framework may represent a fundamental computational unit for the development of artificial SNN, opening new perspectives in pattern recognition tasks.