A temporal estimate of integrated information for intracranial functional connectivity
Arsiwalla, Xerxes D. (Institut de Bioenginyeria de Catalunya)
Pacheco Estefan, Daniel (Institut de Bioenginyeria de Catalunya)
Principe, Alessandro (Hospital del Mar (Barcelona, Catalunya))
Rocamora Zúñiga, Rodrigo Alberto (Hospital del Mar (Barcelona, Catalunya))
Verschure, Paul F. M. J. (Institut de Bioenginyeria de Catalunya)

Títol variant:	Artificial Neural Networks and Machine Learning - ICANN 2018 : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II
Títol variant:	27th International Conference on Artificial Neural Networks, ICANN 2018
Data:	2018
Descripció:	10 pàg.
Resum:	A major challenge in computational and systems neuroscience concerns the quantification of information processing at various scales of the brain's anatomy. In particular, using human intracranial recordings, the question we ask in this paper is: How can we estimate the informational complexity of the brain given the complex temporal nature of its dynamics? To address this we work with a recent formulation of network integrated information that is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. In this work, we extend this formulation for temporal networks and then apply it to human brain data obtained from intracranial recordings in epilepsy patients. Our findings show that compared to random re-wirings of the data, functional connectivity networks, constructed from human brain data, score consistently higher in the above measure of integrated information. This work suggests that temporal integrated information may indeed be a good starting point as a future measure of cognitive complexity.
Nota:	This work is supported by the European Research Council's CDAC project: "The Role of Consciousness in Adaptive Behavior: A Combined Empirical, Computational and Robot based Approach", (ERC-2013-ADG 341196).
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Llengua:	Anglès
Document:	Capítol de llibre ; recerca ; Versió publicada
Matèria:	Brain networks ; Complexity measures ; Computational neuroscience ; Functional connectivity
Publicat a:	Lecture notes in computer science, Vol. 11140 (2018) , p. 403-412, ISSN 1611-3349

DOI: 10.1007/978-3-030-01421-6_39

10 p, 356.8 KB

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