Graph Analysis For Simulated Neural Networks With STDP

Abstract

Many real-world systems can be represented as networks and studied using graph theory. Brain graphs are widely used to analyze brain connectomes using graph theory. Electrophysiological data, tract-tracing, and MRI data have been used to extract functional brain graphs. This study analyzes the properties of brain graphs generated using a neural network simulator. Using a simulator solves the problems related to pre-processing, data acquisition, and length of time series which exist in extracting brain graphs using other data collection methods. Synaptic plasticity is an important part of the functioning and growth of a neural network, and spike-time-dependent plasticity (STDP) has emerged as one of the most widely used plasticity mechanisms due to its physiological realistic induction and evidence of its presence in vivo. This thesis presents the graphical analysis for a spatiotemporal neural dataset and compares the properties of the connectome with a random graph model of similar size. We implement different STDP algorithms and use STDP to refine a simulation equivalent to neuronal growth for 28 days in vitro. We analyze the effect of STDP on the network's connections and structural properties.