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  Training deep neural density estimators to identify mechanistic models of neural dynamics

Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., et al. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv: the preprint server for biology, 838383. doi:10.1101/838383.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-D52C-A Version Permalink: https://hdl.handle.net/21.11116/0000-000C-FCE7-4
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 Creators:
Gonçalves, Pedro J.1, Author
Lueckmann, Jan-Matthis2, Author
Deistler, Michael2, Author
Nonnenmacher, Marcel2, Author
Öcal, Kaan1, Author
Bassetto, Giacomo1, Author
Chintaluri, Chaitanya2, Author
Podlaski, William F.2, Author
Haddad, Sara A.2, Author
Vogels, Tim P.2, Author
Greenberg, David S.2, Author
Macke, Jakob H.2, Author
Affiliations:
1Max 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              
2External Organizations, ou_persistent22              

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 Abstract: Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

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Language(s): eng - English
 Dates: 2019-11-122020-03-07
 Publication Status: Published online
 Pages: -
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 Rev. Type: No review
 Identifiers: DOI: 10.1101/838383
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Title: bioRxiv : the preprint server for biology
  Abbreviation : bioRxiv
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: 838383 Start / End Page: - Identifier: ZDB: 2766415-6
CoNE: https://pure.mpg.de/cone/journals/resource/2766415-6