1. Computational and Systems Biology
  2. Neuroscience

Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

  1. Jonathan Oesterle
  2. Christian Behrens
  3. Cornelius Schröder
  4. Thoralf Hermann
  5. Thomas Euler
  6. Katrin Franke
  7. Robert G Smith
  8. Günther Zeck
  9. Philipp Berens  Is a corresponding author
  1. University of Tübingen, Germany
  2. The Natural and Medical Sciences Institute, Germany
  3. University of Pennsylvania, United States
https://doi.org/10.7554/eLife.54997
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  1. Jonathan Oesterle
  2. Christian Behrens
  3. Cornelius Schröder
  4. Thoralf Hermann
  5. Thomas Euler
  6. Katrin Franke
  7. Robert G Smith
  8. Günther Zeck
  9. Philipp Berens
(2020)
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
eLife 9:e54997.
https://doi.org/10.7554/eLife.54997
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Abstract

While multicompartment models have long been used to study the biophysics of neurons, it is still challenging to infer the parameters of such models from data including uncertainty estimates. Here, we performed Bayesian inference for the parameters of detailed neuron models of a photoreceptor and an OFF- and an ON-cone bipolar cell from the mouse retina based on two-photon imaging data. We obtained multivariate posterior distributions specifying plausible parameter ranges consistent with the data and allowing to identify parameters poorly constrained by the data. To demonstrate the potential of such mechanistic data-driven neuron models, we created a simulation environment for external electrical stimulation of the retina and optimized stimulus waveforms to target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprosthetics.

Data availability

All code and data will be made available upon publication on https://github.com/berenslab.

Article and author information

Author details

  1. Jonathan Oesterle

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  2. Christian Behrens

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3623-352X

  3. Cornelius Schröder

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  4. Thoralf Hermann

    Neurophysics group, The Natural and Medical Sciences Institute, Reutlingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  5. Thomas Euler

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4567-6966

  6. Katrin Franke

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  7. Robert G Smith

    Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Günther Zeck

    Neurophysics group, The Natural and Medical Sciences Institute, Reutlingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3998-9883

  9. Philipp Berens

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    For correspondence
    philipp.berens@uni-tuebingen.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0199-4727

Funding

Bundesministerium für Bildung und Forschung (01GQ1601)

  • Philipp Berens

Bundesministerium für Bildung und Forschung (01IS18052)

  • Philipp Berens

Deutsche Forschungsgemeinschaft (EXC 2064 - 390727645)

  • Philipp Berens

Deutsche Forschungsgemeinschaft (BE5601/4-1)

  • Philipp Berens

Baden-Württemberg Stiftung (NEU013)

  • Günther Zeck
  • Philipp Berens

National Institutes of Health (EY022070)

  • Robert G Smith

National Institutes of Health (EY023766)

  • Robert G Smith

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Alexander Borst, Max Planck Institute of Neurobiology, Germany

Ethics

Animal experimentation: All animal procedures were approved by the governmental review board (Regierungspräsidium Tübingen, Baden-Württemberg, Konrad-Adenauer-Str. 20, 72072 Tübingen, Germany) and performed according to the laws governing animal experimentation issued by the German Government.All procedures were approved by the governmental review board (Regierungspräsidium Tübingen, Baden-Württemberg, Konrad-Adenauer-Str. 20, 72072 Tübingen, Germany, AZ 35/9185.82-7) and performed according to the laws governing animal experimentation issued by the German Government.

Version history

  1. Received: January 8, 2020
  2. Accepted: October 26, 2020
  3. Accepted Manuscript published: October 27, 2020 (version 1)
  4. Version of Record published: November 18, 2020 (version 2)

Copyright

© 2020, Oesterle et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Jonathan Oesterle
  2. Christian Behrens
  3. Cornelius Schröder
  4. Thoralf Hermann
  5. Thomas Euler
  6. Katrin Franke
  7. Robert G Smith
  8. Günther Zeck
  9. Philipp Berens
(2020)
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
eLife 9:e54997.
https://doi.org/10.7554/eLife.54997

Share this article

https://doi.org/10.7554/eLife.54997

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