Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
- University of Tübingen, Germany
- The Natural and Medical Sciences Institute, Germany
- University of Pennsylvania, United States
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
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.
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.
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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Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
eLife 9:e54997.
https://doi.org/10.7554/eLife.54997
Further reading
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- Computational and Systems Biology
- Neuroscience
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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