A Computational Model of the Brain Cortex and Its Synchronization

Abstract

Obtaining the computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly. This would help the development of better treatments for neurological illnesses and disorders. We created a cortical model using Python language using the Brian simulator. The Brian simulator is specialized in simulating the neuronal connections and synaptic interconnections. The dynamic connection model has multiple parameters in order to ensure an accurate simulation (Bowman, 2016). We concentrated on the connection weights and studied their effect on the interactivity and connectivity of the cortical neurons in the same cortical layer and across multiple layers. As synchronization helps us to measure the degree of correlation between two or more neuronal groups, the synchronization between the neuronal groups, which are connected across layers, is considered. Despite its obvious importance, there are no sufficient studies concerned about the synchronization in the simulated cortical models. Such studies can help in examining the hypothesis and the dynamical behavior of the simulated model. In this paper, we simulated a cortical model and dynamical behavior and then studied the effect of input noise on its internal neuronal networks and their synchronization.

A Decision-Making Model Based on Spiking Neural Network (SNN) for Remote Patient Monitoring

“…Our choice turned then to the "Integrate and fire" model [27]. This model offers computational efficiency, has been implemented on several platforms, especially Brian Simulator [32], [38], which we used to perform our experiments and has been widely studied [39,40]. In this model, neurons can be schematized as in Fig.…”