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Simulations of the activity of a brain cortex slice have been carried out in different conditions that mimic physiological and pathological situations of the tissue. The assumption is the coupling of the excitation dynamics of the single cells drives the electrocortical activity. Cellular Automata (CA) has implemented a local rule, which has been taken from a continuous model of cortical activity. A logistic relation links local field potentials and pulse density of cells. Different simulations of the model have been run. The presence of a non-specific input applied to the whole cortex slice simulates the general level of excitability of the tissue. Other pathophysiological events such as necrotic nonexcitable groups of cells and clusters of cells synchronously activating have been also obtained. Patterns resulting from the system evolution have been classified by non-linear systems analysis based on False Nearest Neighbors Dimension and Interspike Intervals. Results underline that different complexity patterns characterize dynamic behaviors originating from parameters settings.