Neuromorphic computing has attracted great attention for its massive parallelism and high energy efficiency. As the fundamental components of neuromorphic computing systems, artificial neurons play a key role in information processing. However, the development of artificial neurons that can simultaneously incorporate low hardware overhead, high reliability, high speed, and low energy consumption remains a challenge. To address this challenge, we propose and demonstrate a piezoelectric neuron with a simple circuit structure, consisting of a piezoelectric cantilever, a parallel capacitor, and a series resistor. It operates through the synergy between the converse piezoelectric effect and the capacitive charging/discharging. Thanks to this efficient and robust mechanism, the piezoelectric neuron not only implements critical leaky integrate-and-fire functions (including leaky integration, threshold-driven spiking, all-or-nothing response, refractory period, strength-modulated firing frequency, and spatiotemporal integration), but also demonstrates small cycle-to-cycle and device-to-device variations (∼1.9% and ∼10.0%, respectively), high endurance (10¹⁰), high speed (integration/firing: ∼9.6/∼0.4 μs), and low energy consumption (∼13.4 nJ/spike). Furthermore, spiking neural networks based on piezoelectric neurons are constructed, showing capabilities to implement both supervised and unsupervised learning. This study therefore opens up a new way to develop high-performance artificial neurons by using piezoelectrics, which may facilitate the realization of advanced neuromorphic computing systems.

中文翻译：

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用于神经形态计算的压电神经元


神经形态计算因其大规模并行性和高能效而受到广泛关注。人工神经元作为神经形态计算系统的基本组成部分，在信息处理中发挥着关键作用。然而，开发能够同时整合低硬件开销、高可靠性、高速度和低能耗的人工神经元仍然是一个挑战。为了应对这一挑战，我们提出并演示了一种具有简单电路结构的压电神经元，由压电悬臂、并联电容器和串联电阻器组成。它通过反向压电效应和电容充电/放电之间的协同作用来运作。得益于这种高效而稳健的机制，压电神经元不仅实现了关键的泄漏积分和发射功能（包括泄漏积分、阈值驱动的尖峰、全有或全无响应、不应期、强度调制发射频率和时空积分），而且还表现出小的周期间和器件间变化（分别为 ∼1.9% 和 ∼10.0%）、高耐久性 （10¹⁰）、高速（积分/触发：∼9.6/∼0.4 μs）和低能耗（∼13.4 nJ/尖峰）。此外，构建了基于压电神经元的脉冲神经网络，显示出实现有监督和无监督学习的能力。因此，这项研究开辟了一条利用压电体开发高性能人工神经元的新途径，这可能有助于实现先进的神经形态计算系统。