---
_id: '8127'
abstract:
- lang: eng
  text: 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.
acknowledgement: We thank Mahmood S Hoseini and Michael Stryker for sharing their
  data for Figure 2, and Philipp Berens, Sean Bittner, Jan Boelts, John Cunningham,
  Richard Gao, Scott Linderman, Eve Marder, Iain Murray, George Papamakarios, Astrid
  Prinz, Auguste Schulz and Srinivas Turaga for discussions and/or comments on the
  manuscript. This work was supported by the German Research Foundation (DFG) through
  SFB 1233 ‘Robust Vision’, (276693517), SFB 1089 ‘Synaptic Microcircuits’, SPP 2041
  ‘Computational Connectomics’ and Germany's Excellence Strategy – EXC-Number 2064/1
  – Project number 390727645 and the German Federal Ministry of Education and Research
  (BMBF, project ‘ADIMEM’, FKZ 01IS18052 A-D) to JHM, a Sir Henry Dale Fellowship
  by the Wellcome Trust and the Royal Society (WT100000; WFP and TPV), a Wellcome
  Trust Senior Research Fellowship (214316/Z/18/Z; TPV), a ERC Consolidator Grant
  (SYNAPSEEK; WPF and CC), and a UK Research and Innovation, Biotechnology and Biological
  Sciences Research Council (CC, UKRI-BBSRC BB/N019512/1). We gratefully acknowledge
  the Leibniz Supercomputing Centre for funding this project by providing computing
  time on its Linux-Cluster.
article_number: e56261
article_processing_charge: No
article_type: original
author:
- first_name: Pedro J.
  full_name: Gonçalves, Pedro J.
  last_name: Gonçalves
  orcid: 0000-0002-6987-4836
- first_name: Jan-Matthis
  full_name: Lueckmann, Jan-Matthis
  last_name: Lueckmann
  orcid: 0000-0003-4320-4663
- first_name: Michael
  full_name: Deistler, Michael
  last_name: Deistler
  orcid: 0000-0002-3573-0404
- first_name: Marcel
  full_name: Nonnenmacher, Marcel
  last_name: Nonnenmacher
  orcid: 0000-0001-6044-6627
- first_name: Kaan
  full_name: Öcal, Kaan
  last_name: Öcal
  orcid: 0000-0002-8528-6858
- first_name: Giacomo
  full_name: Bassetto, Giacomo
  last_name: Bassetto
- first_name: Chaitanya
  full_name: Chintaluri, Chaitanya
  id: BA06AFEE-A4BA-11EA-AE5C-14673DDC885E
  last_name: Chintaluri
  orcid: 0000-0003-4252-1608
- first_name: William F.
  full_name: Podlaski, William F.
  last_name: Podlaski
  orcid: 0000-0001-6619-7502
- first_name: Sara A.
  full_name: Haddad, Sara A.
  last_name: Haddad
  orcid: 0000-0003-0807-0823
- first_name: Tim P
  full_name: Vogels, Tim P
  id: CB6FF8D2-008F-11EA-8E08-2637E6697425
  last_name: Vogels
  orcid: 0000-0003-3295-6181
- first_name: David S.
  full_name: Greenberg, David S.
  last_name: Greenberg
- first_name: Jakob H.
  full_name: Macke, Jakob H.
  last_name: Macke
  orcid: 0000-0001-5154-8912
citation:
  ama: Gonçalves PJ, Lueckmann J-M, Deistler M, et al. Training deep neural density
    estimators to identify mechanistic models of neural dynamics. <i>eLife</i>. 2020;9.
    doi:<a href="https://doi.org/10.7554/eLife.56261">10.7554/eLife.56261</a>
  apa: Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K.,
    Bassetto, G., … Macke, J. H. (2020). Training deep neural density estimators to
    identify mechanistic models of neural dynamics. <i>ELife</i>. eLife Sciences Publications.
    <a href="https://doi.org/10.7554/eLife.56261">https://doi.org/10.7554/eLife.56261</a>
  chicago: Gonçalves, Pedro J., Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher,
    Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, et al. “Training Deep Neural
    Density Estimators to Identify Mechanistic Models of Neural Dynamics.” <i>ELife</i>.
    eLife Sciences Publications, 2020. <a href="https://doi.org/10.7554/eLife.56261">https://doi.org/10.7554/eLife.56261</a>.
  ieee: P. J. Gonçalves <i>et al.</i>, “Training deep neural density estimators to
    identify mechanistic models of neural dynamics,” <i>eLife</i>, vol. 9. eLife Sciences
    Publications, 2020.
  ista: Gonçalves PJ, Lueckmann J-M, Deistler M, Nonnenmacher M, Öcal K, Bassetto
    G, Chintaluri C, Podlaski WF, Haddad SA, Vogels TP, Greenberg DS, Macke JH. 2020.
    Training deep neural density estimators to identify mechanistic models of neural
    dynamics. eLife. 9, e56261.
  mla: Gonçalves, Pedro J., et al. “Training Deep Neural Density Estimators to Identify
    Mechanistic Models of Neural Dynamics.” <i>ELife</i>, vol. 9, e56261, eLife Sciences
    Publications, 2020, doi:<a href="https://doi.org/10.7554/eLife.56261">10.7554/eLife.56261</a>.
  short: P.J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G.
    Bassetto, C. Chintaluri, W.F. Podlaski, S.A. Haddad, T.P. Vogels, D.S. Greenberg,
    J.H. Macke, ELife 9 (2020).
date_created: 2020-07-16T12:26:04Z
date_published: 2020-09-17T00:00:00Z
date_updated: 2023-08-22T07:54:52Z
day: '17'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.7554/eLife.56261
ec_funded: 1
external_id:
  isi:
  - '000584989400001'
  pmid:
  - '32940606'
file:
- access_level: open_access
  checksum: c4300ddcd93ed03fc9c6cdf1f77890be
  content_type: application/pdf
  creator: cziletti
  date_created: 2020-10-27T11:37:32Z
  date_updated: 2020-10-27T11:37:32Z
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  file_name: 2020_eLife_Gonçalves.pdf
  file_size: 17355867
  relation: main_file
  success: 1
file_date_updated: 2020-10-27T11:37:32Z
has_accepted_license: '1'
intvolume: '         9'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
  call_identifier: H2020
  grant_number: '819603'
  name: Learning the shape of synaptic plasticity rules for neuronal architectures
    and function through machine learning.
publication: eLife
publication_identifier:
  eissn:
  - 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Training deep neural density estimators to identify mechanistic models of neural
  dynamics
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 9
year: '2020'
...