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    We developed a low-cost adaptive exponential integrate-and-fire neuron (SC-AdEx) using stochastic computing. This efficient neuron model precisely reproduces biological behaviors with lower hardware costs for large-scale neuromorphic systems.

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    Area of Science:

    • Neuroscience
    • Computer Engineering
    • Neuromorphic Computing

    Background:

    • Neurons are fundamental to the nervous system, crucial for both brain research and engineering novel brain-inspired hardware.
    • Implementing efficient biological neuron models at scale presents a significant challenge, requiring a balance between functionality and cost.

    Purpose of the Study:

    • To introduce a novel, low-cost adaptive exponential integrate-and-fire neuron (SC-AdEx) for large-scale neuromorphic systems.
    • To leverage stochastic computing for efficient arithmetic operations within the neuron model.

    Main Methods:

    • Developed the SC-AdEx neuron model utilizing stochastic computing principles for arithmetic operations.
    • Conducted biological behavior analysis to validate the model's ability to replicate various firing patterns.
    • Synthesized and implemented the SC-AdEx model on FPGA for proof-of-concept validation.

    Main Results:

    • The SC-AdEx model accurately reproduces a wide range of biological neuron behaviors.
    • Achieved higher computational performance compared to existing state-of-the-art adaptive exponential integrate-and-fire hardware neurons.
    • Demonstrated significantly lower hardware costs for implementation.

    Conclusions:

    • The SC-AdEx neuron offers an efficient and cost-effective solution for large-scale neuromorphic systems.
    • Stochastic computing provides a viable approach for implementing complex neuron models with reduced hardware resources.
    • The FPGA implementation validates the practical feasibility and performance benefits of the proposed model.