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Interpreting neural and behavioral data using Hidden Markov Models.

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Τίτλος: Interpreting neural and behavioral data using Hidden Markov Models.
Συγγραφείς: Versteijnen ML; Department of Circuits, Structure and Function, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Science, Amsterdam, the Netherlands., Heimel JA; Department of Circuits, Structure and Function, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Science, Amsterdam, the Netherlands; Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour and Faculty of Science, Radboud University, Nijmegen, the Netherlands. Electronic address: heimel@nin.knaw.nl.
Πηγή: Brain research [Brain Res] 2026 Oct 15; Vol. 1889, pp. 150468. Date of Electronic Publication: 2026 Jul 16.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 0045503 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-6240 (Electronic) Linking ISSN: 00068993 NLM ISO Abbreviation: Brain Res Subsets: MEDLINE
Imprint Name(s): Original Publication: Amsterdam Elsevier/North-Holland Biomedical Press.
Ιατρικοί όροι (MeSH): Brain*/physiology , Behavior*/physiology , Hidden Markov Models* , Models, Neurological*, Neurons/physiology ; Animals ; Humans ; Markov Chains
Περίληψη: Measurements of neural activity and behavior are becoming increasingly detailed, producing ever-larger multidimensional datasets. Yet these measurements offer only glimpses into the workings of complex brains. Much remains hidden. Hidden Markov Models (HMMs) are versatile tools for inferring information about unobserved brain states from behavior, neural recordings, or their combination. HMMs and their extensions have been frequently and successfully used in neuroscience for this purpose. An HMM describes a Markov process in which hidden states transition between one another and generate observations that depend on the current state. HMMs are particularly well suited for naturalistic behavioral paradigms that lack a fixed trial structure, and they can provide interpretable insights into an animal's internal state. In this review, we introduce the general framework of HMMs and explain how to implement them. We comprehensively review how HMMs have been applied to neural and behavioral data, and how hidden states identified in behavior or neural activity can be used to understand neural processing. We highlight example studies in which HMMs have provided insight into the brain processes underlying behavior and perception. Our emphasis is on how specific data properties or research questions motivate different modeling choices. We also discuss the relationship between HMMs and alternative methods for latent state discovery.
(Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Brain states; Dimension reduction; Latent states; Neural data; Organization of behavior
Entry Date(s): Date Created: 20260716 Date Completed: 20260808 Latest Revision: 20260808
Update Code: 20260809
DOI: 10.1016/j.brainres.2026.150468
PMID: 42462989
Βάση Δεδομένων: MEDLINE
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  Data: Measurements of neural activity and behavior are becoming increasingly detailed, producing ever-larger multidimensional datasets. Yet these measurements offer only glimpses into the workings of complex brains. Much remains hidden. Hidden Markov Models (HMMs) are versatile tools for inferring information about unobserved brain states from behavior, neural recordings, or their combination. HMMs and their extensions have been frequently and successfully used in neuroscience for this purpose. An HMM describes a Markov process in which hidden states transition between one another and generate observations that depend on the current state. HMMs are particularly well suited for naturalistic behavioral paradigms that lack a fixed trial structure, and they can provide interpretable insights into an animal's internal state. In this review, we introduce the general framework of HMMs and explain how to implement them. We comprehensively review how HMMs have been applied to neural and behavioral data, and how hidden states identified in behavior or neural activity can be used to understand neural processing. We highlight example studies in which HMMs have provided insight into the brain processes underlying behavior and perception. Our emphasis is on how specific data properties or research questions motivate different modeling choices. We also discuss the relationship between HMMs and alternative methods for latent state discovery.<br /> (Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved.)
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      – TitleFull: Interpreting neural and behavioral data using Hidden Markov Models.
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