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<p>Neuromorphic computing aims to build electronic systems that mimic the brain’s remarkable efficiency and adaptability. A key feature of brain is homeostatic plasticity, which stabilizes neural activity; the Bienenstock–Cooper–Munro (BCM) rule accurately describes this by adjusting a neuron’s sensitivity threshold based on past activity. However, implementing the homeostatic rule in synaptic device remains challenging. Here we show that a CuInP₂S₆ memristor naturally realizes the complete BCM rule. We reveal that the device’s intrinsic junction capacitance generates reverse spikes that cause spike-rate-dependent conductance depression, combining with second-order ionic dynamics to replicate key BCM features. Through simulations, we demonstrate that adding a similar capacitance can universally enable this behavior in conventional memristors. Finally, we build a physical reservoir computing system based on this strategy that exhibits robust, noise-tolerant spatiotemporal processing. This work provides a viable path for deploying adaptive homeostatic plasticity in hardware, enhancing the reliability of neuromorphic systems.</p>

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Realization of the Bienenstock-Cooper-Munro rule in a single memristor

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Jiangshun Huang,
  • Anping Huang,
  • Qiaofeng Yang,
  • Pengzhan Li,
  • Qin Gao,
  • Mei Wang,
  • Zhisong Xiao,
  • Xingwang Zhang,
  • Juan Gao,
  • Xueli Geng,
  • Limin Liu,
  • Yi Du,
  • Ruifeng Lu,
  • Paul K. Chu,
  • Zengfeng Di

摘要

Neuromorphic computing aims to build electronic systems that mimic the brain’s remarkable efficiency and adaptability. A key feature of brain is homeostatic plasticity, which stabilizes neural activity; the Bienenstock–Cooper–Munro (BCM) rule accurately describes this by adjusting a neuron’s sensitivity threshold based on past activity. However, implementing the homeostatic rule in synaptic device remains challenging. Here we show that a CuInP₂S₆ memristor naturally realizes the complete BCM rule. We reveal that the device’s intrinsic junction capacitance generates reverse spikes that cause spike-rate-dependent conductance depression, combining with second-order ionic dynamics to replicate key BCM features. Through simulations, we demonstrate that adding a similar capacitance can universally enable this behavior in conventional memristors. Finally, we build a physical reservoir computing system based on this strategy that exhibits robust, noise-tolerant spatiotemporal processing. This work provides a viable path for deploying adaptive homeostatic plasticity in hardware, enhancing the reliability of neuromorphic systems.