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  Inference of a mesoscopic population model from population spike trains

René, A., Longtin, A., & Macke, J. H. (2020). Inference of a mesoscopic population model from population spike trains. Neural computation, 32(8), 1448-1498. doi:10.1162/neco_a_01292.

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https://arxiv.org/abs/1910.01618 (Preprint)
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René, Alexandre1, Author
Longtin, Andre1, Author
Macke, Jakob H2, Author              
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1External Organizations, ou_persistent22              
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society, Ludwig-Erhard-Allee 2, 53175 Bonn, DE, ou_2173683              

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 Abstract: Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

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Language(s): eng - English
 Dates: 2020-08
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000548539700002
DOI: 10.1162/neco_a_01292
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Title: Neural computation
  Abbreviation : Neural Comput
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 32 (8) Sequence Number: - Start / End Page: 1448 - 1498 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591