Raspberry Pi Implementation of The Wilson-Cowan Neural Network With Chemical Synapse

Information transmission in living things occurs through synapse connections between neurons and the transfer of ions from one neuron to another. Synapses can be Electrical and Chemical. Transmission at electrical synapses is direct and very fast. Unlike chemical synapses, it does not inhibit the other neuron. Chemical synapses are more flexible, versatile, and more active in transmitting complex information than electrical synapses. The Wilson-Cowan (WC) neuron model explains neural activity in the Neocortex with mean excitatory and inhibitory interaction. It is essential because it has a simple mathematical structure and can explain the activities of a large neural population. While there are models of electrically coupled WC neural populations in the literature, there are no studies on a WC neural population with the advantages of chemical coupling. This paper presents a simple structured chemical coupling model WC neural population that does not add any new variables to the system. The standard deviation method provides the synchronous and asynchronous behavior of this network structure, which is one of the stability control approaches. In addition, electrical and chemically coupled WC neural population; The hardware solution has been realized on a standard equipped Raspberry Pi (RPi), which has high configurability, can solve complex mathematical expressions easily and quickly, has no memory limitation, low thermal noise sensitivity, low-cost production process. In this respect, our study provided support to the practice field as well as its theoretical innovation. In addition, the integration of chemical coupling into the WC neuron is the first in that the coupling does not have computational difficulties compared to other chemical coupling models and that synchronous-asynchronous behaviours are realized over RPi.