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000190549 0247_ $$2doi$$a10.1109/IEMBS.2011.6091708
000190549 037__ $$aCONF
000190549 245__ $$aAssessing directed information as a method for inferring functional connectivity in neural ensembles
000190549 269__ $$a2011
000190549 260__ $$bIEEE$$c2011
000190549 336__ $$aConference Papers
000190549 520__ $$aNeurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an information theoretic measure known as directed information has been proposed as a way to capture directionality in the relationship, thereby moving towards a model of effective connectivity. This measure is grounded upon the concept of Granger causality and can be estimated by modeling neural spike trains as point process generalized linear models. However, the added benefit of using directed information to infer connectivity over conventional methods such as correlation is still unclear. Here, we propose a novel estimation procedure for the directed information. Using physiologically realistic simulations, we demonstrate that directed information can outperform correlation in determining connections between neural spike trains while also providing directionality of the relationship, which cannot be assessed using correlation.
000190549 700__ $$aSo, K.
000190549 700__ $$0241387$$g122796$$aGastpar, M.
000190549 700__ $$aCarmena, J. M.
000190549 7112_ $$d30 08 - 3 09 2011$$cBoston, MA$$a2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
000190549 773__ $$t2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society$$q7324-7327
000190549 909C0 $$xU12434$$0252408$$pLINX
000190549 909CO $$pconf$$pIC$$ooai:infoscience.tind.io:190549
000190549 917Z8 $$x144898
000190549 937__ $$aEPFL-CONF-190549
000190549 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000190549 980__ $$aCONF