Random geometric graphs and their applications in neuronal modelling
Random graph theory is an important tool to study different problems arising from real world.In this thesis we study how to model connections between neurons (nodes) and synaptic con-nections (edges) in the brain using inhomogeneous random distance graph models. We presentfour models which have in common the characteristic of having a probability of connectionsbetween the nodes dependent on the diRandom graph theory is an important tool to study different problems arising from real world.In this thesis we study how to model connections between neurons (nodes) and synaptic connections (edges) in the brain using inhomogeneous random distance graph models. We presentfour models which have in common the characteristic of having a probability of connectionsbetween the nodes dependent on the dis