Investigation of a moderate cortical model synchronization created using BRIAN simulator

Kbah, Sadeem Nabeel Saleem

doi:10.1109/tiptekno.2019.8895185

Cited by 2 publications

(1 citation statement)

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“…With this structure, it is possible to define the types of connections between the connected layers to be either sparse or dense . Sparse which means a spike will either occur or not between the connected layers compared to the dense which means a spike will certainly occur between all the connected layers; it will be the default choice in our case since it is more suitable for modifying the dynamical connections [ 27 , 28 ]. The dynamical part of the synaptic connections (here for the groups, in our example it is g ampa ) is represented by another differentiation equation, Equation ( 5 ), where g ampa denotes the dynamic variable corresponding to synaptic connection and it is initialized with random values.…”

Section: Creating the Cortical Layersmentioning

confidence: 99%

A Computational Model of the Brain Cortex and Its Synchronization

Kbah

2020

BioMed Research International

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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.

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Section: Creating the Cortical Layersmentioning

confidence: 99%