Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.

ABSTRACT: Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learn

SUBMITTER: Tolley N 

PROVIDER: S-EPMC10153146 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature