Neuronal networks and Self-Organized Criticality: The rising of long-term memory in neuronal ensembles

Since the late 90s both single and multi-electrode neuronal network recording signals have been characterized as endowed with long-term memory, i.e. a long-lasting decreasing correlation in the signal second-order statistics (e.g., autocorrelation function). Such a characteristic, typical of many fractal processes, indicate that the signal actual value strongly depends on its ldquopast historyrdquo. At the same time, neuronal networks have been modeled as Self-Organized Criticality (SOC) systems, i.e., systems that independently organize themselves in a critical state. In this paper we analyze the estimations of long-term memory behavior for in-vitro and in-vivo neuronal networks (both by using original and literature data) and discuss such a results by the light of the SOC modeling.