Análise da conectividade e resiliência de redes complexas formadas por neurônios

Abstract

The computational study of biological neural networks involves using real properties of neurons to simulate the dynamics of neuronal cells. Although recent neuroscience studies investigate the relationship between neurons and blood vessels, little is known about the influence of vascularization on the collective behavior of neurons. This study aims to create a computational model to abstract biological neural networks into complex networks, in order to obtain relevant data about the connectivity and resilience of these networks. Computational routines will be developed to simulate these networks and study the influence of neuron removal on the collective dynamics of the network. The removals represent the deactivation of neurons due to complete interruptions in blood flow resulting from the obstruction of blood vessels in a specific region. Data was obtained on the number of connected components and the largest connected components for scenarios of full networks and networks with removals depending on the vertices degrees, verifying greater network resilience to non-targeted attacks on neurons with high degrees. Additionally, data on neuronal dynamics, such as firing rate during time intervals and the number of firings in a simulation, were collected for different removal scenarios. The model produced can be used in neuroscience studies on neurovascular interactions. This study represents a first step in understanding how vascularization affects the dynamics of biological neural networks and their level of resilience to failures.