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

View ORCID ProfilePedro J. Gonçalves, View ORCID ProfileJan-Matthis Lueckmann, View ORCID ProfileMichael Deistler, View ORCID ProfileMarcel Nonnenmacher, View ORCID ProfileKaan Öcal, Giacomo Bassetto, View ORCID ProfileChaitanya Chintaluri, View ORCID ProfileWilliam F. Podlaski, View ORCID ProfileSara A. Haddad, Tim P. Vogels, David S. Greenberg, View ORCID ProfileJakob H. Macke
doi: https://doi.org/10.1101/838383
Pedro J. Gonçalves
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
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  • ORCID record for Pedro J. Gonçalves
  • For correspondence: pedro.goncalves@caesar.de jan-matthis.lueckmann@tum.de michael.deistler@tum.de macke@tum.de
Jan-Matthis Lueckmann
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
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  • ORCID record for Jan-Matthis Lueckmann
  • For correspondence: pedro.goncalves@caesar.de jan-matthis.lueckmann@tum.de michael.deistler@tum.de macke@tum.de
Michael Deistler
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
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  • ORCID record for Michael Deistler
  • For correspondence: pedro.goncalves@caesar.de jan-matthis.lueckmann@tum.de michael.deistler@tum.de macke@tum.de
Marcel Nonnenmacher
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
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Kaan Öcal
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
3Mathematical Institute, University of Bonn, Bonn, Germany
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  • ORCID record for Kaan Öcal
Giacomo Bassetto
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
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Chaitanya Chintaluri
4Centre for Neural Circuits and Behaviour, University of Oxford
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William F. Podlaski
4Centre for Neural Circuits and Behaviour, University of Oxford
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  • ORCID record for William F. Podlaski
Sara A. Haddad
5Max Planck Institute for Brain Research, Frankfurt, Germany
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Tim P. Vogels
4Centre for Neural Circuits and Behaviour, University of Oxford
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David S. Greenberg
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
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Jakob H. Macke
1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Germany
2Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
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  • ORCID record for Jakob H. Macke
  • For correspondence: pedro.goncalves@caesar.de jan-matthis.lueckmann@tum.de michael.deistler@tum.de macke@tum.de
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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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Posted March 07, 2020.
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Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J. Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F. Podlaski, Sara A. Haddad, Tim P. Vogels, David S. Greenberg, Jakob H. Macke
bioRxiv 838383; doi: https://doi.org/10.1101/838383
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Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J. Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F. Podlaski, Sara A. Haddad, Tim P. Vogels, David S. Greenberg, Jakob H. Macke
bioRxiv 838383; doi: https://doi.org/10.1101/838383

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