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Journal Article

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

MPS-Authors

Gonçalves,  Pedro J
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;
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Lueckmann,  J-M
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;
External Organizations;

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Nonnenmacher,  Marcel
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;
External Organizations;

Bassetto,  Giacomo
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;
External Organizations;

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Macke,  Jakob H
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;
External Organizations;

External Ressource
Fulltext (public)

elife-56261-v3.pdf
(Publisher version), 17MB

elife-56261-v2.pdf
(Preprint), 18MB

Supplementary Material (public)
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Citation

Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., et al. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, 9: e56261. doi:10.7554/eLife.56261.


Cite as: http://hdl.handle.net/21.11116/0000-0007-6401-7
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.