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<p>Neuromorphic systems based on spike-timing-dependent plasticity offer energy-efficient learning but face limitations in terms of adapting to high-frequency inputs, restricting their effectiveness in processing complex temporal information. Synaptic fatigue dynamics, analogous to biological short-term plasticity, can increase the effectiveness, but this feature is difficult to efficiently incorporate in hardware. Here we report a hybrid architecture in which arrays of memristors with distinct dynamics are paired to create synaptic elements with short-term fatigue and long-term memory. The elements consist of an interfacial dynamic memristor with high uniformity and intrinsic fatigue behaviour coupled to a hafnia-based one-transistor–one-non-volatile memristor. The design enables a hardware-efficient implementation of fatigue spike-timing-dependent plasticity, enhancing the temporal learning capabilities of spiking neural networks. We show that the resulting neural network can be used for unsupervised online learning with high adaptability to both rate- and timing-coded spikes, high noise resilience and superior performance over conventional spike-timing-dependent plasticity approaches.</p>

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Spiking neural networks with fatigue spike-timing-dependent plasticity learning using hybrid memristor arrays

  • 期刊论文

  • 发表日期：

    2026年1月15日

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  • Bingjie Dang,
  • Teng Zhang,
  • Fanqi Meng,
  • Keqin Liu,
  • Liutao Yu,
  • Qinghua Zhang,
  • Si Wu,
  • Lin Gu,
  • Ru Huang,
  • Yuchao Yang

摘要

Neuromorphic systems based on spike-timing-dependent plasticity offer energy-efficient learning but face limitations in terms of adapting to high-frequency inputs, restricting their effectiveness in processing complex temporal information. Synaptic fatigue dynamics, analogous to biological short-term plasticity, can increase the effectiveness, but this feature is difficult to efficiently incorporate in hardware. Here we report a hybrid architecture in which arrays of memristors with distinct dynamics are paired to create synaptic elements with short-term fatigue and long-term memory. The elements consist of an interfacial dynamic memristor with high uniformity and intrinsic fatigue behaviour coupled to a hafnia-based one-transistor–one-non-volatile memristor. The design enables a hardware-efficient implementation of fatigue spike-timing-dependent plasticity, enhancing the temporal learning capabilities of spiking neural networks. We show that the resulting neural network can be used for unsupervised online learning with high adaptability to both rate- and timing-coded spikes, high noise resilience and superior performance over conventional spike-timing-dependent plasticity approaches.