Mostly, the focus is on improving the measurement and imaging techniques like EEG (electroencephalogram) and fMRI (functional magnetic resonance imaging) and collecting and processing data with these techniques. Especially, modeling the cortex has been the most studied subject among the computational neuroscientist. To understand the connectivity and functionality of the neuronal groups, models at different levels have been proposed.
In the last decade, the work on obtaining computational models for the brain has been increased tremendously.
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. Such studies can help in examining the hypothesis and the dynamical behavior of the simulated model. Despite its obvious importance, there are no sufficient studies concerned about the synchronization in the simulated cortical models. 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. 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. The dynamic connection model has multiple parameters in order to ensure an accurate simulation (Bowman, 2016).
#Model of the brain simulator#
The Brian simulator is specialized in simulating the neuronal connections and synaptic interconnections. We created a cortical model using Python language using the Brian simulator. This would help the development of better treatments for neurological illnesses and disorders. Obtaining the computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly.
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