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Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

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    This study analyzes temporal encoding methods for spiking neural networks (SNNs). Step-forward (SW) encoding is most effective for various signals, offering robustness and ease of optimization for efficient SNN system design.

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    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence

    Background:

    • Spiking neural networks (SNNs) process information via spike trains.
    • Efficient SNN design requires optimal encoding of real-valued signals into spike trains.

    Purpose of the Study:

    • To provide a systematic analysis and guidelines for optimal temporal encoding in SNNs.
    • To compare the effectiveness of different encoding methods for various signal types.

    Main Methods:

    • A three-step encoding workflow: method selection, parameter optimization using error metrics, and validation.
    • Analysis of four encoding methods: Ben's Spiker algorithm (BSA), threshold-based, step-forward (SW), and moving-window (MW).
    • Quantitative analysis using step-wise, noisy smooth, trended smooth, and event-like smooth signals.

    Main Results:

    • Ben's Spiker algorithm (BSA) is ineffective for step-wise signals but can adapt to smooth signals with scaled coefficients.
    • Step-forward (SW) encoding, producing bipolar spikes, demonstrated superior effectiveness, robustness, and ease of optimization across all tested signal types.
    • Signal-to-noise ratio (SNR) is recommended as an effective error metric for parameter optimization.

    Conclusions:

    • The study offers guidelines for selecting and optimizing temporal encoding methods for SNNs.
    • Step-forward (SW) encoding emerges as a highly effective method for diverse signal processing in SNNs.
    • Further research is needed to develop objective validation methods beyond visual checks.