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

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

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 ne...

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Bibliographic Details
Main Authors: Pedro J Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, David S Greenberg, Jakob H Macke
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2020-09-01
Series:eLife
Subjects:
bayesian inference
deep learning
stomatogastric ganglion
model identification
neural dynamics
mechanistic models
Online Access:https://elifesciences.org/articles/56261

Internet

https://elifesciences.org/articles/56261

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