DocumentCode :
2500454
Title :
Assessing directed information as a method for inferring functional connectivity in neural ensembles
Author :
So, Kelvin ; Gastpar, Michael ; Carmena, Jose M.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, Berkeley, CA, USA
fYear :
2011
fDate :
Aug. 30 2011-Sept. 3 2011
Firstpage :
7324
Lastpage :
7327
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.
Keywords :
brain; information theory; neural nets; neurophysiology; statistical analysis; brain neuronal networks; directed information; functional connectivity inference; information theoretic measure; neural ensembles; neural spike train; synaptic connections; Accuracy; Brain modeling; Computational modeling; Correlation; Measurement; Neurons; Topology; Action Potentials; Algorithms; Brain; Computer Simulation; Humans; Linear Models; Models, Statistical; Models, Theoretical; Nerve Net; Neural Pathways; Neurons; Regression Analysis; Reproducibility of Results; Signal Processing, Computer-Assisted;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2011.6091708
Filename :
6091708
Link To Document :
بازگشت