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Neural barcoding representing cortical spatiotemporal dynamics based on continuous-time Markov chains.

Jordan M Culp1, Donovan M Ashby2, Antis G George3

  • 1Department of Psychiatry, University of Calgary, Calgary, AB T2N 2T9, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Mathison Centre for Mental Health Research and Education, Calgary, AB T2N 4Z6, Canada; Department of Cell Biology and Anatomy, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada; Department of Physics and Astronomy, University of Calgary, Calgary, AB T2N 1N4, Canada.

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|February 5, 2026
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Summary
This summary is machine-generated.

Researchers developed a new computational model to analyze complex brain activity patterns. This continuous-time Markov chain (CTMC) model reveals conserved neural dynamics and creates a "neural barcode" for brain research.

Keywords:
CP: neuroscienceMarkovian dynamicscalcium imagingcomputational modelingcortical imagingmesoscale imagingneural barcodingspontaneous activity

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Neuroscience

Background:

  • Neural populations exhibit complex spatiotemporal activity patterns across large brain regions.
  • Understanding the temporal transitions of these activity motifs is challenging due to limitations in current analytical methods.
  • Existing computational models often simplify or omit either spatial or temporal dynamics.

Purpose of the Study:

  • To develop a probabilistic framework for analyzing temporal sequences in large-scale cortical activity.
  • To identify conserved dynamical structures within complex neural activity.
  • To introduce a low-dimensional descriptor of neural dynamics for broader applications.

Main Methods:

  • Utilized a continuous-time Markov chain (CTMC) modeling framework.
  • Applied the CTMC model to large-scale cortical activity data obtained via mesoscale imaging.
  • Analyzed the parameters of the CTMC model to derive a descriptive metric.

Main Results:

  • Identified a conserved dynamical structure in cortical activity across different animals.
  • Revealed modular transitions in neural activity that function as pseudo-absorbing states.
  • Demonstrated that CTMC model parameters can serve as a sensitive "neural barcode".

Conclusions:

  • The CTMC model offers a robust method for probabilistically describing temporal sequences in complex cortical dynamics.
  • The derived "neural barcode" provides a powerful, low-dimensional tool for characterizing neural dynamics.
  • This approach is applicable to various cortical imaging applications, including the study of pathological brain dynamics.