Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010.
Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544.
Proc Natl Acad Sci U S A. 2024 Oct 8;121(41):e2409330121. doi: 10.1073/pnas.2409330121. Epub 2024 Oct 4.
Habituation-a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld-is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.
习惯化——一种现象，即动力系统对重复刺激的反应逐渐减弱，当刺激停止时会恢复——在从动物到单细胞生物的生命系统中普遍存在。尽管它很普遍，但这种基本形式的学习的通用机制仍未得到明确界定。受先前关于自适应响应阶跃输入系统的研究的启发，我们从非线性动力学的角度来研究习惯化。这种方法使我们能够形式化习惯化的经典特征，这些特征已在不同的生物体和刺激场景中得到实验验证。我们使用这个框架来研究不同的能够习惯化的动力电路。具体来说，我们表明，具有静态非线性的记忆变量的驱动线性动力学，在输入和输出处起作用，可以以数学可解释的方式实现许多特征。这项工作为理解这种原始学习行为的动力学基础奠定了基础，并为在生物系统中识别习惯化电路提供了蓝图。
