Functional Connectivity Analysis of Neuronal Interactions

Abstract

Advancements in Computational Neuroscience have resulted in the construction of physiologically realistic and computationally efficient neuronal networks. The developments of diverse neuronal models at various implementation scales have caused an affirmative leap in understanding the numerous causes of brain disorders and anomalies, yet the treatment of the brain is regarded as complicated. Observing the responses of a network of realistic neurons constructed using compartmental modelling makes it possible to overcome the shortcomings of descriptive models, such as their inability to highlight synaptic behaviour and their lack of biological realism in spiking behaviour, which in fact play a crucial role in understanding various cognitive mechanisms via the computational unit called neurons. Henceforth, the dynamics of a microscopic level of interacting neurons coupled hierarchically via feedback and feed forward connections are examined in this article. A tiny network of two and four coupled Hodgkin Huxley (HH) neurons integrated into Freeman KIII set topology have been built using the Spike Timing Dependent plasticity(STDP) algorithm. The entire network has been modelled in Neuron modelling environment using Python as the programming interface, and the functional connectivity between the layers has been analysed.