Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories

Genkin, Mikhail, Hughes, Owen, Engel, Tatiana A (October 2021) Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories. Nature Communications, 12 (1). p. 5986. ISSN 2041-1723

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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.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > computational biology > algorithms organism description > animal behavior bioinformatics > computational biology organism description > animal behavior > decision making organism description > animal behavior > learning bioinformatics > computational biology > statistical analysis
CSHL Authors:	Genkin, Mikhail Engel, Tatiana
Communities:	CSHL labs > Engel lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	13 October 2021
Date Deposited:	20 Oct 2021 13:57
Last Modified:	23 Jan 2024 21:20
PMCID:	PMC8514604
URI:	https://repository.cshl.edu/id/eprint/40391

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