Penny, W;
Sengupta, B;
(2016)
Annealed Importance Sampling for Neural Mass Models.
PLoS Computational Biology
, 12
(3)
, Article e1004797. 10.1371/journal.pcbi.1004797.
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Abstract
Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution.
Type: | Article |
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Title: | Annealed Importance Sampling for Neural Mass Models |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1371/journal.pcbi.1004797 |
Publisher version: | http://dx.doi.org/10.1371/journal.pcbi.1004797 |
Language: | English |
Additional information: | © 2016 Penny, Sengupta. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences |
URI: | https://discovery.ucl.ac.uk/id/eprint/1481988 |
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