Dynamics of spiking map-based neural networks in problems of supervised learning

Abstract Recurrent networks of artificial spiking neurons trained to perform target functions are a perspective tool for understanding dynamic principles of information processing in computational neuroscience. Here, we develop a system of this type based on a map-based model of neural activity allowing for producing various biologically relevant regimes. Target signals used to supervisely train the network are sinusoid functions of different frequencies. Impacts of individual neuron dynamics, coupling strength, network size and other key parameters on the learning error are studied. Our findings suggest, among others, that firing rate heterogeneity as well as mixing of spiking and nonspiking regimes of neurons comprising the network can improve its performance for a wider range of target frequencies. At a single neuron activity level, successful training gives rise to well separated domains with qualitatively different dynamics.