Stimuli Dependent Synergy and Redundancy Dominated Causal Effects in Time Series
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We characterize the degree of synergy- and redundancy-dominated causal influence a time series has upon the interaction between other time series. We prove that for a class of time series, the early past of the stimuli yields a synergistic effect upon the interaction, whereas the late past has a redundancy-dominated effect. Our information theoretic quantities are easy to compute in practice, and we provide simulation studies on synthetic time series and real-world signals. As an example, we use our measures on intracranial EEG data to demonstrate that the stimuli from specific deep electrodes cause a synergistic exchange of information to take place between different brain regions during seizures.
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