ArticleNature communications2021
Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
- Neuronal signatures of successful one-shot memory in mid-level visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Article
- Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.ArXiv · 2025Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Neural population dynamics underlying evidence accumulation in multiple rat brain regions.eLife · 2024Article
- Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.Entropy (Basel, Switzerland) · 2024Article
- The dynamics and geometry of choice in premotor cortex.bioRxiv : the preprint server for biology · 2023Article
- A unifying perspective on neural manifolds and circuits for cognition.Nature reviews. Neuroscience · 2023Review
- Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories.Nature communications · 2021Article
- The population doctrine in cognitive neuroscience.Neuron · 2021Review
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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