Hidden Markov Model Links Zebrafish Brain Behavior
Dommanget-Kott et al. (published January 5, 2026) apply three-state Hidden Markov Models to zebrafish larvae behavior and ARTR calcium imaging, identifying leftward, rightward, and forward states. The HMMs accurately capture bout-type persistence, temperature dependence, and inter-individual variability, and neural-state sequences generate synthetic swim trajectories mirroring behavioral statistics. The study provides open-source code and data for cross-modal modeling of brain–behavior relationships.
Key Points
- 1Identify three hidden locomotor states corresponding to left, right, and forward swim bouts.
- 2Show ARTR calcium recordings exhibit matching three-state dynamics, linking neural activity to behavior.
- 3Enable generation of synthetic swim trajectories from neural HMMs for hypothesis testing and modeling.
Scoring Rationale
Solid cross-modal, peer-reviewed study with usable code, but applies established HMM methods within a niche experimental system.
Sources
Public references used for this report.
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