Studies in cortical network models - Romaro, Cecília (2020)
This thesis consists of five chapter-articles, each proposing simple solutions to nontrivial questions and problems in computational neuroscience. The first chapter presents a reimplementation of the Potjans-Diesmann (PD) model of the local cortical microcircuitry, and a rescaling method for the number of neurons in the model that is capable of maintaining both the connection probabilities and the behavior of the network activity even when rescaled to 1 % of original size. The second chapter, based on mean field potential, formally explains the scaling method and presents a new method to correct and compensate for the activity of boundary neurons in networks with spatial extension without introducing toroidal connections and/or oscillations. The third chapter introduces spatial extension to the PD model, solves the boundary problem, and studies the spatial (topographic) resolution of the network activity as a consequence of the structural resolution. The fourth chapter, based on phase transition and meta-stability, innovatively studies the false steady state and activity lifetime in networks that do not receive forced external input to keep them active. The fifth chapter contains a characterization of the primary somatosensory cortex network of the rat in terms of a statistical survey of the parameters. It also presents a model of the somatosensory cortex using the stochastic Galves-Löcherbach (GL) neuron, which was constructed based on the somatotopic parameters raised. At the end of the chapter, a method for replacing deterministic leaky integrate-and-fire neurons by GL neurons in neural network models is presented.