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September 2011 Efficient methods for sampling spike trains in networks of coupled neurons
Yuriy Mishchenko, Liam Paninski
Ann. Appl. Stat. 5(3): 1893-1919 (September 2011). DOI: 10.1214/11-AOAS467

Abstract

Monte Carlo approaches have recently been proposed to quantify connectivity in neuronal networks. The key problem is to sample from the conditional distribution of a single neuronal spike train, given the activity of the other neurons in the network. Dependencies between neurons are usually relatively weak; however, temporal dependencies within the spike train of a single neuron are typically strong. In this paper we develop several specialized Metropolis–Hastings samplers which take advantage of this dependency structure. These samplers are based on two ideas: (1) an adaptation of fast forward–backward algorithms from the theory of hidden Markov models to take advantage of the local dependencies inherent in spike trains, and (2) a first-order expansion of the conditional likelihood which allows for efficient exact sampling in the limit of weak coupling between neurons. We also demonstrate that these samplers can effectively incorporate side information, in particular, noisy fluorescence observations in the context of calcium-sensitive imaging experiments. We quantify the efficiency of these samplers in a variety of simulated experiments in which the network parameters are closely matched to data measured in real cortical networks, and also demonstrate the sampler applied to real calcium imaging data.

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Yuriy Mishchenko. Liam Paninski. "Efficient methods for sampling spike trains in networks of coupled neurons." Ann. Appl. Stat. 5 (3) 1893 - 1919, September 2011. https://doi.org/10.1214/11-AOAS467

Information

Published: September 2011
First available in Project Euclid: 13 October 2011

zbMATH: 1229.92015
MathSciNet: MR2884926
Digital Object Identifier: 10.1214/11-AOAS467

Keywords: block-Gibbs sampling , fluorescence imaging , HMM , Neural spike train data

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 3 • September 2011
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