Modelling populations of spiking neurons in Autistic Spectrum Disorder (ASD)

This Master’s thesis introduces the computational modelling of neural data analyzed using a novel choice of mathematical model: the population-tracking model [1]. The main purpose of the model is to capture the neuronal dynamics of large populations of neurons (N > 25). We modelled three different types of spikes datasets: synthetic data, multielectrode array data, and calcium imaging data collected in the context of a neurobiological experiment investigating the effect of SynGap knockouts (heterozygous genotype) on neural firing behaviours. Autistic Spectrum Disorder (ASD) is a range of intellectual disorders which involves genetic mutations as well as changes in neuronal networks dynamics. How can mathematical modelling be used in order to make sense of this neurobiological disorder? This is what we attempted to understand further through the implementation of the populationtracking model. Multielectrode array modelling analyses results mainly supported the findings from past research [2]. As for calcium imaging data analyses, we were only able to gather preliminary results which supported early experimental findings [3] and suggested that there may indeed be a difference in the degree of neuronal remodelling between wild type mice and heterozygous mice.