标题： 有源忆阻器漏电积分-发放神经元电路的正则量子化 显示英文标题

标题： Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit

摘要： 我们提出了一种量化忆阻型漏积分-放电（LIF）神经元的理论框架，结合了神经形态工程和开放量子系统的原理。 从经典的忆阻型LIF电路出发，我们应用规范量化技术，推导出基于电路量子电动力学的量子模型。 数值模拟展示了弱耦合和绝热状态下量化忆阻器和LIF神经元的关键动态特性，包括记忆效应和脉冲行为。 这项工作建立了量子神经形态计算的基础模型，为生物启发的量子脉冲神经网络和量子机器学习的新范式提供了途径。 显示英文摘要

摘要： We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory effects and spiking behaviour. This work establishes a foundational model for quantum neuromorphic computing, offering a pathway towards biologically inspired quantum spiking neural networks and new paradigms in quantum machine learning.