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A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces.

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    Area of Science:

    • Neuroscience
    • Electrical Engineering
    • Computer Science

    Background:

    • Neural recording systems are crucial for Brain-Machine Interfaces (BMIs).
    • Traditional systems prioritize signal fidelity for remote processing, leading to high data bandwidth requirements.
    • There is a need for integrated, low-power neural recording solutions for on-site data compression and analysis.

    Purpose of the Study:

    • To propose and validate an alternative neural recording system architecture.
    • To enable direct local interfacing with neuromorphic spiking neural processing circuits.
    • To achieve on-site data compression, signal processing, and information extraction for reduced bandwidth transmission.

    Main Methods:

    • Fabrication of a neural recording system including a low-noise amplifier, delta-modulator analog-to-digital converter, and band-pass filter.
    • Characterization of the bio-amplifier's programmable gain (45-54 dB) and low input-referred noise (2.1 μV RMS).
    • Integration of asynchronous handshaking logic for event-based communication and interfacing with a reconfigurable neuromorphic processor.

    Main Results:

    • The fabricated neural recording system demonstrated low power consumption (90 μW for the amplifier) and efficient data handling.
    • Experimental validation confirmed the properties of the low-noise amplifier, delta-modulator, and band-pass filter circuits.
    • Successful interfacing with a neuromorphic processor configured as a recurrent neural network for pattern recognition using processed neural events.

    Conclusions:

    • The developed neural recording system effectively compresses data and performs local signal processing, suitable for direct interfacing with neuromorphic circuits.
    • This approach significantly reduces the bandwidth required for transmitting neural data, enabling efficient remote computation or actuation.
    • The system's capability for pattern recognition through on-chip processing highlights its potential for advanced, integrated Brain-Machine Interfaces.