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<p>The brain exhibits rich dynamical properties that underpin its remarkable temporal processing capabilities. However, spiking neural networks (SNNs) inspired by the brain have not yet matched their biological counterparts in temporal processing and remain vulnerable to noise perturbations. This study addresses these limitations by introducing Rhythm-SNN, which draws inspiration from the brain’s neural oscillation mechanism. Specifically, we employ heterogeneous oscillatory signals to modulate spiking neurons, enforcing them to activate periodically at distinct frequencies. This approach not only significantly reduces neuronal firing rates but also enhances the capability and robustness of SNNs in temporal processing. Extensive experiments and theoretical analyses demonstrate that Rhythm-SNN achieves state-of-the-art performance across a broad range of tasks, with a markedly reduced energy cost, even under strong perturbations. Notably, in the Intel Neuromorphic Deep Noise Suppression Challenge, Rhythm-SNN outperforms deep learning solutions by achieving over two orders of magnitude in energy reduction while delivering award-winning denoising performance.</p>

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Efficient and robust temporal processing with neural oscillations modulated spiking neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Yinsong Yan,
  • Qu Yang,
  • Yujie Wu,
  • Hanwen Liu,
  • Malu Zhang,
  • Haizhou Li,
  • Kay Chen Tan,
  • Jibin Wu

摘要

The brain exhibits rich dynamical properties that underpin its remarkable temporal processing capabilities. However, spiking neural networks (SNNs) inspired by the brain have not yet matched their biological counterparts in temporal processing and remain vulnerable to noise perturbations. This study addresses these limitations by introducing Rhythm-SNN, which draws inspiration from the brain’s neural oscillation mechanism. Specifically, we employ heterogeneous oscillatory signals to modulate spiking neurons, enforcing them to activate periodically at distinct frequencies. This approach not only significantly reduces neuronal firing rates but also enhances the capability and robustness of SNNs in temporal processing. Extensive experiments and theoretical analyses demonstrate that Rhythm-SNN achieves state-of-the-art performance across a broad range of tasks, with a markedly reduced energy cost, even under strong perturbations. Notably, in the Intel Neuromorphic Deep Noise Suppression Challenge, Rhythm-SNN outperforms deep learning solutions by achieving over two orders of magnitude in energy reduction while delivering award-winning denoising performance.