title: Assessing directed information as a method for inferring functional connectivity in neural ensembles.
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.
authors.affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA. sokelvin@eecs.berkeley.edu
authors.forename: Kelvin
authors.initials: K
authors.lastname: So
authors.forename: Michael
authors.initials: M
authors.lastname: Gastpar
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted