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 activi

SUBMITTER: Goncalves PJ 

PROVIDER: S-EPMC7581433 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature