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We analyze a simple dynamical network model which describes the limitedcapacity of nodes to process the input information. For a suitable choice ofthe parameters, the information flow pattern is characterized by exponentialdistribution of the incoming information and a fat-tailed distribution of theoutgoing information, as a signature of the law of diminishing marginalreturns. The analysis of a real EEG data-set shows that similar phenomena maybe relevant for brain signals.

Information flow in a network model and the law of diminishing marginal returns