NDLI: Assessing directed information as a method for inferring functional connectivity in neural ensembles

Content Provider	IEEE Xplore Digital Library
Author	So, K. Gastpar, M. Carmena, J.M.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA (So, K.; Gastpar, M.) \|\| Department of Electrical Engineering and Computer Sciences, the Program in Cognitive Science and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720 USA (Carmena, J.M.)
Abstract	Neurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an information theoretic measure known as directed information has been proposed as a way to capture directionality in the relationship, thereby moving towards a model of effective connectivity. This measure is grounded upon the concept of Granger causality and can be estimated by modeling neural spike trains as point process generalized linear models. However, the added benefit of using directed information to infer connectivity over conventional methods such as correlation is still unclear. Here, we propose a novel estimation procedure for the directed information. Using physiologically realistic simulations, we demonstrate that directed information can outperform correlation in determining connections between neural spike trains while also providing directionality of the relationship, which cannot be assessed using correlation.
Starting Page	7324
Ending Page	7327
File Size	1322899
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091708
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Correlation Topology Computational modeling Accuracy Brain modeling Measurement
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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