不同基质与尺度下习惯化的动力学原理
Dynamical principles of habituation across substrates and scales
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中文总结 AI 辅助
本综述探究跨生物、非生命系统的习惯化底层动力学原理，构建满足习惯化约束的非线性基元，关联其与生物、物理及算法实现的模型。
中文摘要 AI 辅助
习惯化是一种基础学习形式，即系统对重复刺激的反应会逐渐减弱，但当刺激被撤除后最终会恢复。该现象在动物中已被长期研究，如今也越来越多地在单细胞生物以及电子电路、神经形态材料等非生命装置中被观察到，这表明存在跨领域重复出现的底层动力学原理。本综述探究这些原理是什么：鉴于习惯化对系统反应施加的定性约束，满足这些约束的最小动力学结构是什么？我们将习惯化的经典特征形式化为对输入-输出行为的行为约束，证明线性时不变系统在结构上与这些约束不兼容，并构建非线性基元——由静态非线性组合而成的线性衰减记忆动力学——这些基元在不同场景下均表现出习惯化特征。我们将这些基元与特定生物系统模型、物理及算法实现相联系，涵盖从模拟电路到机器学习中的瞬时计算。
英文摘要
Habituation is a basic form of learning in which a system's response to repeated stimulation progressively diminishes but eventually recovers when the stimulus is withheld. Long studied in animals, it has increasingly been observed in unicellular organisms and non-living devices such as electronic circuits and neuromorphic materials, suggesting underlying dynamical principles that recur across domains. This review asks what those principles are: given qualitative constraints imposed by habituation on a system's response, what is the minimal dynamical structure that satisfies them? We formalize the classical hallmarks of habituation as behavioral constraints on input--output behavior, show that linear time-invariant systems are structurally incompatible with these constraints, and construct nonlinear motifs---linear fading-memory dynamics composed with static nonlinearities---that exhibit the hallmarks across diverse settings. We relate these motifs to models of specific biological systems and to physical and algorithmic realizations, from analog circuits to transient computation in machine learning.
