Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 19, Issue 2, 2011, Pages 121-135

  Chen, Zhe ^a,b   Putrino, David F ^a,b   Ghosh, Soumya ^c   Barbieri, Riccardo ^a,b   Brown, Emery N ^a,b,d  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

^d HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

1 regularization; 2 regularization; Conjugate gradient; functional connectivity; interior point method; maximum likelihood estimate (MLE); neuronal interactions; penalized maximum likelihood; point process generalized linear model; variational Bayes

Indexed keywords

CONJUGATE GRADIENT; FUNCTIONAL CONNECTIVITY; INTERIOR-POINT METHOD; MAXIMUM LIKELIHOOD ESTIMATE (MLE); NEURONAL INTERACTIONS; PENALIZED MAXIMUM LIKELIHOOD; POINT PROCESS GENERALIZED LINEAR MODEL; VARIATIONAL BAYES;

ALGORITHMS; BAYESIAN NETWORKS; CONJUGATE GRADIENT METHOD; ESTIMATION; MATHEMATICAL MODELS; STATISTICAL METHODS;

MAXIMUM LIKELIHOOD ESTIMATION;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOLOGICAL MODEL; CAT; COMPUTER SIMULATION; CYTOLOGY; ELECTROPHYSIOLOGY; MOTOR CORTEX; NERVE CELL; NERVE TRACT; PHYSIOLOGY; REPRODUCIBILITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; BAYES THEOREM; CATS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; ELECTROPHYSIOLOGICAL PHENOMENA; LIKELIHOOD FUNCTIONS; LINEAR MODELS; LOGISTIC MODELS; MODELS, NEUROLOGICAL; MOTOR CORTEX; NEURAL NETWORKS (COMPUTER); NEURAL PATHWAYS; NEURONS; REPRODUCIBILITY OF RESULTS;

EID: 79954548587     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2010.2086079     Document Type: Article

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