Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Machine Learning in Science, Excellence Cluster Machine Learning, Tübingen University, Tübingen, Germany
Marcel Nonnenmacher
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany; Model-Driven Machine Learning, Institute of Coastal Research, Helmholtz Centre Geesthacht, Geesthacht, Germany
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany; Mathematical Institute, University of Bonn, Bonn, Germany
Giacomo Bassetto
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany
Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom; Institute of Science and Technology Austria, Klosterneuburg, Austria
Max Planck Institute for Brain Research, Frankfurt, Germany
Tim P Vogels
Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom; Institute of Science and Technology Austria, Klosterneuburg, Austria
David S Greenberg
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Model-Driven Machine Learning, Institute of Coastal Research, Helmholtz Centre Geesthacht, Geesthacht, Germany
Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn, Germany; Machine Learning in Science, Excellence Cluster Machine Learning, Tübingen University, Tübingen, Germany; Max Planck Institute for Intelligent Systems, Tübingen, Germany
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.
