Biological relevance of spiking neural networks

Throughout this dissertation I examine the α-pulse coupled Leaky Integrate and Fire network as a relevant model accounting for biological observations in the neuroscience’s field. In doing so, I study in detail the role of the structural properties (parameters) of the α-LIF model in shaping the dynamics in three experimentally inspired applications: First I provide a theoretical substrate to understand the role of sparse connectivity in generating non-trivial dynamics in excitatory networks, akin to the Giant Depolarizing Potentials (GDP) in developing hyppocampal networks. I then analyze the importance of inhibition as the origin of irregular spiking and perturbation amplification of balanced neural networks, which are prototypical of cortical dynamics. Finally I describe how sparse connectivity, inhibition and synaptic dynamics can provide the conditions of emergence of cell assembly dynamics, which are related to the encoding mechanism observed in the striatum.