Computational tool to study high dimensional dynamic in NMM
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Neuroscience has shown great progress in recent years. Several of the theoretical bases have arisen from the examination of dynamic systems, using Neural Mass Models (NMMs). Due to the largescale brain dynamics of NMMs and the difficulty of studying nonlinear systems, the local linearization approach to discretize the state equation was used via an algebraic formulation, as it intervenes favorably in the speed and efficiency of numerical integration. To study the spacetime organization of the brain and generate more complex dynamics, three structural levels (cortical unit, population and system) were defined and assumed, in which the new assumed representation for conduction delays and new ways of connecting were defined. This is a new time-delay NMM, which can simulate several types of EEG activities since kinetics information was considered at three levels of complexity. Results obtained in this analysis provide additional theoretical foundations and indicate specific characteristics for understanding neurodynamic.
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