{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Genkin M"],"funding":["Katya H. Davey Fellowship","NIBIB NIH HHS","Swartz Foundation Postdoctoral Fellowship","U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering"],"pagination":["5986"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8514604"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["Many complex systems operating far from the equilibrium exhibit stochastic dynamics that can be described by a Langevin equation. Inferring Langevin equations from data can reveal how transient dynamics of such systems give rise to their function. However, dynamics are often inaccessible directly and can be only gleaned through a stochastic observation process, which makes the inference challenging. Here we present a non-parametric framework for inferring the Langevin equation, which explicitly models the stochastic observation process and non-stationary latent dynamics. The framework accounts for the non-equilibrium initial and final states of the observed system and for the possibility that the system's dynamics define the duration of observations. Omitting any of these non-stationary components results in incorrect inference, in which erroneous features arise in the dynamics due to non-stationary data distribution. We illustrate the framework using models of neural dynamics underlying decision making in the brain."],"journal":["Nature communications"],"pubmed_title":["Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories."],"pmcid":["PMC8514604"],"funding_grant_id":["R01 EB026949"],"pubmed_authors":["Hughes O","Engel TA","Genkin M"],"additional_accession":[]},"is_claimable":false,"name":"Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories.","description":"Many complex systems operating far from the equilibrium exhibit stochastic dynamics that can be described by a Langevin equation. Inferring Langevin equations from data can reveal how transient dynamics of such systems give rise to their function. However, dynamics are often inaccessible directly and can be only gleaned through a stochastic observation process, which makes the inference challenging. Here we present a non-parametric framework for inferring the Langevin equation, which explicitly models the stochastic observation process and non-stationary latent dynamics. The framework accounts for the non-equilibrium initial and final states of the observed system and for the possibility that the system's dynamics define the duration of observations. Omitting any of these non-stationary components results in incorrect inference, in which erroneous features arise in the dynamics due to non-stationary data distribution. We illustrate the framework using models of neural dynamics underlying decision making in the brain.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-19T06:05:48.292Z","creation":"2025-04-19T06:05:48.292Z"},"accession":"S-EPMC8514604","cross_references":{"pubmed":["34645828"],"doi":["10.1038/s41467-021-26202-1"]}}