title: A regularized point process generalized linear model for assessing the functional connectivity in the cat motor cortex.
abstract: Identification of multiple simultaneously recorded neural spike train recordings is an important task in understanding neuronal dependency, functional connectivity, and temporal causality in neural systems. An assessment of the functional connectivity in a group of ensemble cells was performed using a regularized point process generalized linear model (GLM) that incorporates temporal smoothness or contiguity of the solution. An efficient convex optimization algorithm was then developed for the regularized solution. The point process model was applied to an ensemble of neurons recorded from the cat motor cortex during a skilled reaching task. The implications of this analysis to the coding of skilled movement in primary motor cortex is discussed.
authors: Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. zhechen@mit.edu
authors: Zhe
authors: Z
authors: Chen
authors: David F
authors: DF
authors: Putrino
authors: Demba E
authors: DE
authors: Ba
authors: Soumya
authors: S
authors: Ghosh
authors: Riccardo
authors: R
authors: Barbieri
authors: Emery N
authors: EN
authors: Brown
mesh_terms: D000465:Algorithms; D000818:Animals; D002415:Cats; D016014:Linear Models; D009044:Motor Cortex / Q000502:physiology*