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Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity.
Neural Comput. 2005 Sep; 17(9):1927-61.NC

Abstract

Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.

Authors+Show Affiliations

Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, MA 02114-2698, USA. murat@neurostat.mgh.harvard.eduNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

15992486

Citation

Okatan, Murat, et al. "Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity." Neural Computation, vol. 17, no. 9, 2005, pp. 1927-61.
Okatan M, Wilson MA, Brown EN. Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Comput. 2005;17(9):1927-61.
Okatan, M., Wilson, M. A., & Brown, E. N. (2005). Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation, 17(9), 1927-61.
Okatan M, Wilson MA, Brown EN. Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity. Neural Comput. 2005;17(9):1927-61. PubMed PMID: 15992486.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. AU - Okatan,Murat, AU - Wilson,Matthew A, AU - Brown,Emery N, PY - 2005/7/5/pubmed PY - 2005/9/1/medline PY - 2005/7/5/entrez SP - 1927 EP - 61 JF - Neural computation JO - Neural Comput VL - 17 IS - 9 N2 - Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity. SN - 0899-7667 UR - https://www.unboundmedicine.com/medline/citation/15992486/Analyzing_functional_connectivity_using_a_network_likelihood_model_of_ensemble_neural_spiking_activity_ L2 - https://direct.mit.edu/neco/article-lookup/doi/10.1162/0899766054322973 DB - PRIME DP - Unbound Medicine ER -