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    Area of Science:

    • Computational Neuroscience
    • Systems Neuroscience
    • Machine Learning for Neuroscience

    Background:

    • Dynamical encoding models use low-dimensional hidden states to represent neural activity and behavior.
    • Existing models analyze neural activity at single scales (spikes or fields), neglecting multiscale encoding.
    • Behavioral encoding occurs across diverse spatiotemporal scales, from single neurons to neural populations.

    Purpose of the Study:

    • To develop a novel algorithm for multiscale dynamical modeling and identification.
    • To simultaneously characterize multiscale spike-field dynamics and extract hidden states.
    • To dissociate task-relevant from task-irrelevant neural dynamics.

    Main Methods:

    • Developed a novel multiscale dynamical modeling and identification algorithm.
    • Integrated spike (binary, millisecond) and field (continuous, slower) data.
    • Employed a modal approach to differentiate dynamics based on task relevance.

    Main Results:

    • The algorithm accurately identifies multiscale dynamical models capturing both spike and field activity.
    • Extracted hidden states are multiscale, integrating information from spikes and fields to predict behavior.
    • The method successfully identifies and quantifies task-relevant neural dynamics.

    Conclusions:

    • A new framework enables simultaneous analysis of neural dynamics across spatiotemporal scales.
    • This multiscale approach provides richer insights into neural representations of behavior.
    • The developed methods may advance neurotechnology and our understanding of neural computation.