Paper Info
Title
A Temporal Estimate Of Integrated Information For Intracranial Functional Connectivity
Abstract
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.
Year
DOI
Venue
2018
10.1007/978-3-030-01421-6_39
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II
Keywords
Field
DocType
Computational neuroscience, Brain networks, Complexity measures, Functional connectivity
Computational neuroscience,Information processing,Ask price,Computer science,Cognitive complexity,Human brain,Multivariate normal distribution,Artificial intelligence,Systems neuroscience,Machine learning
Conference
Volume
ISSN
Citations 
11140
0302-9743
0
PageRank 
References 
Authors
0.34
11
5
Name
Order
Citations
PageRank
Xerxes D. Arsiwalla18417.84
Daniel Pacheco281.46
Alessandro Principe300.34
Rodrigo Rocamora421.39
Paul F. M. J. Verschure5677116.64