发表机构
ICTEAM Institute, UCLouvain; Politecnico di Torino; Department of Physics, Kyoto University（UCLouvain ICTEAM研究所; 都灵理工大学; 京都大学物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过工业级SPICE仿真，发现CMOS模拟尖峰神经元在时变输入下比恒定输入具有更可靠的尖峰时间，并利用热力学不确定关系揭示了振荡状态的可靠性-耗散权衡限制。
AI 中文摘要
尖峰神经元是一种动力学系统，在受到足够兴奋性输入时表现出极限环（即尖峰）。Bryant 和 Segundo 以及 Mainen 和 Sejnowski 的著名神经科学实验揭示，某些生物神经元的尖峰时间在受到时变刺激时比恒定刺激时更为可靠。借助一个基于工业物理瞬态噪声的 SPICE 仿真框架（该框架兼容代工厂晶体管紧凑模型），我们展示了为神经形态计算硬件平台设计的 CMOS 模拟尖峰神经元具有类似行为。在恒定激励电流下，神经元工作在振荡状态，经验周期（即尖峰间隔）在连续周期中遭受累积的相位噪声（抖动），这主要归因于晶体管的热噪声。据报道，时变输入能够通过受控的状态转换机制触发输出尖峰，从而在兴奋性状态下显著减少尖峰时间抖动。振荡或速率编码状态在可靠性-耗散权衡方面的物理限制进一步由随机热力学不确定关系所证实，该定理适用于一般的过阻尼耗散随机动力学系统。
英文摘要
A spiking neuron is a dynamical system exhibiting a limit cycle (a spike) when subject to sufficiently excitatory input. Famous neuroscience experiments by Bryant and Segundo as well as Mainen and Sejnowski revealed that the spike times of some biological neurons are more reliable when subject to time-varying stimuli compared to constant ones. Provided with an industrial physics-based transient noise SPICE simulation framework compatible with foundry transistor compact models, we have demonstrated similar behaviours for a CMOS analog spiking neuron designed for neuromorphic computing hardware platforms. Under constant excitation current, the neuron operates in oscillatory regime and the empirical periods (interspike intervals) suffer from accumulated phase noise (jitter) during successive cycles, mainly due to the thermal noise of transistors. A time-varying input is reported to be capable of triggering output spikes according to a controlled statetransition mechanism, thereby significantly reducing the spike time jitter in the excitability regime. The physical limitation of the oscillatory or rate-coding regime in terms of reliabilitydissipation tradeoff is further evidenced by a stochastic thermodynamic uncertainty relation, a theorem that applies to general overdamped dissipative stochastic dynamical systems.
CommentsAccepted for presentation at IEEE CDC 2026
