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Interpreting neural and behavioral data using Hidden Markov Models
Myrthe L Versteijnen1, J Alexander Heimel2
1Department of Circuits, Structure and Function, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Science, Amsterdam, the Netherlands.
Brain Research
|July 16, 2026
Summary
Hidden Markov Models (HMMs) reveal unobserved brain states from neural activity and behavior. This review details HMMs for neuroscience, aiding understanding of brain processes and perception.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Machine Learning in Biology
Background:
- Modern neuroscience generates vast, complex datasets from neural activity and behavior.
- Inferring underlying brain states from these measurements remains challenging.
- Hidden Markov Models (HMMs) offer a powerful framework for this inference.
Purpose of the Study:
- To introduce the Hidden Markov Model (HMM) framework for neuroscience.
- To review applications of HMMs in analyzing neural and behavioral data.
- To guide model selection based on data properties and research questions.
Main Methods:
- Description of the general Hidden Markov Model (HMM) framework.
- Explanation of HMM implementation and extensions.
- Review of HMMs applied to neural recordings and behavioral data.
Main Results:
- HMMs successfully infer hidden states from multidimensional neuroscience data.
- Identified hidden states provide insights into neural processing and perception.
- HMMs are particularly effective for naturalistic behavioral paradigms.
Conclusions:
- HMMs are versatile tools for uncovering latent brain dynamics.
- Understanding HMMs aids in interpreting complex neural and behavioral data.
- HMMs provide valuable insights into the neural basis of behavior and perception.
