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Updated: Jul 12, 2025

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Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo

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    Summary
    This summary is machine-generated.

    This study introduces a novel, cost-effective algorithm for implementing the FitzHugh-Nagumo (FHN) neuron model in hardware. The new method significantly improves precision and reduces resource overhead for large-scale neural network simulations.

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    Area of Science:

    • Neuroscience
    • Computer Engineering
    • Computational Neuroscience

    Background:

    • Developing large-scale biological neural networks requires efficient hardware implementations of neuron models.
    • The FitzHugh-Nagumo (FHN) model offers biological plausibility but is computationally complex for large-scale applications.

    Purpose of the Study:

    • To present a cost-saving and high-precision approximation algorithm for the digital implementation of the FHN model.
    • To reduce resource overhead in hardware implementations of FHN neurons.

    Main Methods:

    • Converted computational data to floating-point numbers, replacing multiplication with exponent addition and piecewise linear mantissa fitting.
    • Utilized shifters and adders for hardware implementation, minimizing resource overhead.
    • Implemented coupled circular neural networks on FPGA to demonstrate effectiveness.

    Main Results:

    • Achieved a normalized root mean square error (RMSE) of 3.5% of the state-of-the-art (SOTA) for FHN neuron implementation.
    • Improved performance overhead ratio by 1.09 times compared to SOTA.
    • Reduced error by 20% compared to approximate multiplier implementations with a 2.8% overhead increase.
    • Demonstrated a 60% error decrease in coupled networks compared to single SOTA neurons.

    Conclusions:

    • The proposed hardware-friendly algorithm enables low-cost, high-precision hardware simulation of biologically plausible neural networks.
    • This approach offers a new perspective for studying large-scale neural networks.
    • The FHN model implementation with this algorithm shows enhanced biological properties and reduced deployment scale.