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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Mechanistic modeling in neuroscience aims to explain neural or behavioral phenomena in terms of 12 underlying causes. A central challenge in building mechanistic models is to identify which models and parameters can 13 achieve an agreement between the model and experimental data. The complexity of models and data characterizing 14 neural systems makes it infeasible to solve model equations analytically or tune parameters manually. To overcome 15 this limitation, we present a machine learning tool that uses density estimators based on deep neural networks-16 trained using model simulations-to infer data-compatible parameters for a wide range of mechanistic models. Our 17 tool identifies all parameters consistent with data, is scalable both in the number of parameters and data features, 18 and does not require writing new code when the underlying model is changed. It can be used to analyze new data 19 rapidly after training, and can be applied to either raw data or selected data features. We demonstrate our approach 20 for parameter inference on ion channels, receptive fields, and Hodgkin-Huxley models. Finally, we use it to explore the 21 space of circuit configurations which give rise to rhythmic activity in a network model of the crustacean stomatogastric 22 ganglion, and to provide hypotheses for compensation mechanisms. The approach presented here will help close the 23 gap between data-driven and theory-driven models of neural dynamics. 24 25 29 computational model that incorporates the mechanisms we believe to be at play, based on scientific knowledge, 30 intuition, and hypotheses about the components of a system and the laws governing their relationships. The goal of 31 such mechanistic models is to investigate whether a proposed mechanism can explain experimental data, uncover 32 details that may have been missed, inspire new experiments, and eventually provide insights into the inner workings 33 of an observed neural or behavioral phenomenon [1][2][3][4]. Examples for such a symbiotic relationship between model 34 and experiments range from the now classical work of Hodgkin and Huxley [5], to population models investigating 35 rules of connectivity, plasticity and network dynamics [6-10], network models of inter-area interactions [11,12], and 36 models of decision making [13, 14].A crucial step in building a model is adjusting its free parameters to be consistent with experimental observations. 38 This is essential both for investigating whether the model agrees with reality and for gaining insight into processes 39 which cannot be measured experimentally. For some models in neuroscience, it is possible to identify the relevant 40 parameter regimes from careful mathematical analysis of the model equations. But as the complexity of both neural 41 data and neural models increases, it becomes very difficult to find well-fitted parameters by inspection, and automated 42 identification of data-consistent parameters is required. 43 Furthermore, to understand how a model quantitatively expla...