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BRAND: A platform for closed-loop experiments with deep network models.

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    This summary is machine-generated.

    The Backend for Realtime Asynchronous Neural Decoding (BRAND) framework enables real-time use of artificial neural networks (ANNs) in neuroscience experiments. BRAND offers low latency and supports multiple programming languages for advanced brain-computer interfaces.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Machine Learning

    Background:

    • Artificial neural networks (ANNs) are powerful for neural decoding but face deployment challenges in real-time systems.
    • Existing frameworks struggle to support high-level languages for ANNs alongside low-latency data processing.

    Approach:

    • Introduced the Backend for Realtime Asynchronous Neural Decoding (BRAND), a modular framework using Linux processes (nodes) communicating via data streams.
    • BRAND utilizes Redis for fast, language-agnostic inter-process communication, supporting 54 programming languages.
    • The asynchronous design enables parallel execution of acquisition, control, and analysis at different timescales.

    Key Points:

    • BRAND achieves inter-process latency under 600 microseconds for high-volume neural data.
    • A brain-computer interface using BRAND with a recurrent neural network decoder demonstrated less than 8 milliseconds latency.
    • The system successfully supported real-time cursor control in a clinical trial participant, including complex model inference.

    Conclusions:

    • BRAND provides a fast, modular, and language-agnostic platform for integrating advanced ANNs into closed-loop neuroscience experiments.
    • This framework significantly lowers barriers for researchers using cutting-edge machine learning tools in real-time brain-computer interfaces.