SNN Based Brain Connectivity Analysis for ASD Children

Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome that starts at a very early age of 2-3 years. It is a fast growing developmental disorder that shows prevalent cognitive and behavioral abnormalities. Studies shows that the non-linear complexity in EEG signals can provide information regarding the neuronal activity of the brain. The recent trend in research is towards understanding the neuronal behavior in the nervous system, to visualize and understand the brain activities in children with neurodevelopment disorder. Biological neuron models communicate through sequences of spikes. Inspired by this, is the development of a new class of neural models, called spiking neural networks (SNNs). These networks aim towards the accurate modelling of synaptic interactions between neurons, taking into account the time of spike firing. So, this work focuses on the connectivity of the EEG signals using a stochastic learning algorithm and thereby analyze the channels that actively contributes for an Audio and Visual stimuli.