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Related Experiment Video

Updated: Jan 9, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

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Learning Temporal Basis Vectors for Closed-Loop Neural Stimulation.

Matthew J Bryan, Felix Schwock, Azadeh Yazdan-Shahmorad

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
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    We developed a new Temporal Basis Function Model (TBFM) for predicting neural responses to stimulation. This AI model is efficient, accurate, and applicable to closed-loop brain stimulation for neurological conditions.

    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence in Medicine
    • Neurotechnology

    Background:

    • Accurate forecasting of neural responses is crucial for effective brain stimulation.
    • Existing models often lack the efficiency and adaptability required for real-time closed-loop applications.

    Purpose of the Study:

    • Introduce a novel Temporal Basis Function Model (TBFM) for spatiotemporal neural response prediction.
    • Enable model-based control techniques for closed-loop neural stimulation.
    • Demonstrate the model's clinical relevance by optimizing efficiency and latency.

    Main Methods:

    • Developed a TBFM framework learning temporal basis functions.
    • Applied TBFMs to micro-electrocorticography (μECog) data from non-human primate optogenetic stimulation experiments.

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    Related Experiment Videos

    Last Updated: Jan 9, 2026

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  • Compared TBFM performance against complex non-linear dynamical systems models and linear state space models (LSSMs).

    • Main Results:

    • TBFMs achieved accuracy comparable to complex non-linear models and surpassed LSSMs.
    • Required minimal data collection (<20 min) and training time (<5 min).
    • Successfully demonstrated shaping neural activity towards desired regimes in simulated closed-loop experiments.

    Conclusions:

    • TBFMs offer an efficient and accurate approach for modeling neural responses to stimulation.
    • The model's optimization in sample efficiency, training time, and latency bridges the gap towards AI-driven closed-loop stimulation therapies.
    • This framework holds potential for developing novel treatments for neurological conditions.