Abstract
Mechanistic modeling in neuroscience aims to explain neural or behavioral phenomena in terms of underlying causes. A central challenge in building mechanistic models is to identify which models and parameters can achieve an agreement between the model and experimental data. The complexity of models and data characterizing neural systems makes it infeasible to solve model equations analytically or tune parameters manually. To overcome this limitation, we present a machine learning tool that uses density estimators based on deep neural networks—trained using model simulations—to infer data-compatible parameters for a wide range of mechanistic models. Our tool identifies all parameters consistent with data, is scalable both in the number of parameters and data features, and does not require writing new code when the underlying model is changed. It can be used to analyze new data rapidly after training, and can be applied to either raw data or selected data features. We demonstrate our approach for parameter inference on ion channels, receptive fields, and Hodgkin–Huxley models. Finally, we use it to explore the space of parameters which give rise to the same rhythmic activity in a network model of the crustacean stomatogastric ganglion and to search for potential compensation mechanisms. The approach presented here will help close the gap between data-driven and theory-driven models of neural dynamics.