ArticleJournal of neuroscience methods2026
Blind identification of state transitions and latent neural dynamics from electrophysiological recordings.
Article in Journal of neuroscience methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.bioRxiv : the preprint server for biology · 2026Article
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3 authors.
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Abstract
backgroundNeural dynamics change over time and with physiologic state. Modeling of neural dynamics can thus be understood at two levels: (i) identifying the latent process that governs how and when states change, and (ii) identifying the generative circuit mechanisms within each state. NEW
methodHere, we develop a data-driven modeling method that tackles these two levels simultaneously. We formulate a parametric network model of neural dynamics that embeds state-dependent modulation. The modulation itself is controlled by a latent switching process, modeled as a Hidden Markov Model (HMM). A key challenge is that the model itself has internal states that must be estimated from observed data. This leads to a triune optimization problem, consisting of model parameterization of the HMM and neural dynamics, alongside state estimation. Our method brings together several optimization frameworks alongside estimation-theoretic constructs to solve this problem efficiently, enabling blind identification of state transitions and neural dynamics.
resultsWe validate this methodology on ground-truth data with known parameters, and find that it accurately infers the transitions in latent state and the dynamics of each state. We demonstrate its capability of inferring changes in brain dynamics from electrophysiological data by testing it on electroencephalography recordings with labeled state transitions. COMPARISON WITH EXISTING
methodsWhile similar methods exist to infer switches and dynamics on the level of individual neurons, there is no directly comparable method available for mesoscale modeling of neural circuits.
conclusionsOur methodology enables blind modeling of changing neural dynamics allowing for inference of modulatory circuit mechanisms.
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