Digital Hardware Implementation of Gaussian Wilson–Cowan Neocortex Model

Hardware implementation of biological neural models can help in better understanding of the brain functionality, implementing cognitive tasks, and also studying the brain diseases. Gaussian Wilson-Cowan model as one of the well-known population-based models represents neuronal functionality in neocortex. In this paper, Gaussian Wilson-Cowan model is investigated in terms of its digital implementation feasibility. Digital model is proposed for the Gaussian Wilson-Cowan and examined from dynamical and timing behavior point of view. The evaluations indicate that the digitized model is able to reproduce the dynamical bifurcations as the original model is capable of. An efficient digital hardware system is given for the proposed model with minimum required resources using Verilog Hardware Description Language. Digital architectures are physically implemented on an Altera FPGA board. Experimental results show that the proposed circuits take maximum 2% of the available resources of a Stratix Altera board. In addition, static timing analysis indicates that the circuits can work in a maximum frequency of 244 MHz.