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We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to.
Neural basis of a perceptual decision in the parietal cortex (area lip) of the rhesus monkey
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Multivariate information transmission
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Identification of functional information subgraphs in complex networks
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Granger causality and transfer entropy are equivalent for gaussian variables
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Representation of confidence associated with a decision by neurons in the parietal cortex
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Nonnegative decomposition of multivariate information
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Network physiology reveals relations between network topology and physiological function
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Expanding the transfer entropy to identify information circuits in complex systems
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Information flow in a kinetic ising model peaks in the disordered phase
Barnett, L., Lizier, J. T., Harré, M., Seth, A. K., and Bossomaier, T. (2013) · 2013
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Quantifying unique information
Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., and Ay, N. (2014) · 2014
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Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials
Kiani, R., Cueva, C. J., Reppas, J. B., and Newsome, W. T. (2014) · 2014
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Multiscale information decomposition: Exact computation for multivariate gaussian processes
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A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula
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Information decomposition of target effects from multi-source interactions: Perspectives on previous, current and future work
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Gramm: grammar of graphics plotting in matlab
Morel, P. (2018) · 2018
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Synergy as a warning sign of transitions: The case of the two-dimensional ising model
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Network physiology: how organ systems dynamically interact
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Natural grouping of neural responses reveals spatially segregated clusters in prearcuate cortex
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An introduction to transfer entropy
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Quantifying collectivity
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Dual coding theory explains biphasic collective computation in neural decision-making
Daniels, B. C., Flack, J. C., and Krakauer, D. C. (2017) · 2017
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Quantifying high-order interdependencies via multivariate extensions of the mutual information
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High-order interdependencies in the aging brain
Gatica, M., Cofré, R., Mediano, P. A., Rosas, F. E., Orio, P., Diez, I., Swinnen, S., and Cortes, J. M. (2020) · 2020
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Dynamic network interactions among distinct brain rhythms as a hallmark of physiologic state and function
Lin, A., Liu, K. K., Bartsch, R. P., and Ivanov, P. C. (2020) · 2020
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Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically-relevant timescales
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Estimating transfer entropy in continuous time between neural spike trains or other event-based data
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