Spiking neural network trained by metaheuristics for gas sensing

In this manuscript, we propose a bio-inspired method of signal processing in a task for the detection of gases (Methane and Iso-Butane) using an array of four gas sensors by Figaro and Spiking Neural Network (SNN-Izhikevich's model), supported by the synaptic current model. Our SNN is trained by the metaheuristic algorithm of Artificial Bee Colony algorithm (ABC algorithm) in a supervised manner. This is demonstrated using Matlab.