Training deep neural density estimators to identify mechanistic models of neural dynamics
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.
Data availability
The contributions of the work are primarily the development and application of computational models, no new data has been obtained or is being published. All code and associated data will be shared in the GitHub repository accompanying the article.
Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft (SFB~1233)
- Jan-Matthis Lueckmann
- Marcel Nonnenmacher
- Giacomo Bassetto
- Jakob H Macke
Deutsche Forschungsgemeinschaft (SFB~1089)
- Pedro J Gonçalves
- Jakob H Macke
Deutsche Forschungsgemeinschaft (SPP~2041)
- Jakob H Macke
Bundesministerium für Bildung und Forschung (01IS18052 A-D)
- Michael Deistler
- Marcel Nonnenmacher
- Jakob H Macke
Wellcome Trust and the Royal Society (WT100000)
- William F Podlaski
- Tim Vogels
H2020 European Research Council (SYNAPSEEK)
- Chaitanya Chintaluri
- William F Podlaski
- Tim Vogels
Wellcome Trust Senior Research Fellowship (214316/Z/18/Z)
- Tim Vogels
UK Research and Innovation, Biotechnology and Biological Sciences Research Council (UKRI-BBSRC BB/N019512/1)
- Chaitanya Chintaluri
Deutsche Forschungsgemeinschaft (Germany's Excellence Strategy - EXC-Number 2064/1 610 - Project number 390727645)
- Jakob H Macke
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Gonçalves 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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