Paulsen Joseph D, Keim Nathan C
Department of Physics and Soft and Living Matter Program, Syracuse University, Syracuse, NY 13244, USA.
Kavli Institute for Theoretical Physics, Santa Barbara, CA 93106, USA.
Proc Math Phys Eng Sci. 2019 Jun;475(2226):20180874. doi: 10.1098/rspa.2018.0874. Epub 2019 Jun 5.
Many materials that are out of equilibrium can 'learn' one or more inputs that are repeatedly applied. Yet, a common framework for understanding such memories is lacking. Here, we construct minimal representations of cyclic memory behaviours as directed graphs, and we construct simple physically motivated models that produce the same graph structures. We show how a model of worn grass between park benches can produce multiple transient memories-a behaviour previously observed in dilute suspensions of particles and charge-density-wave conductors-and the Mullins effect. Isolating these behaviours in our simple model allows us to assess the necessary ingredients for these kinds of memory, and to quantify memory capacity. We contrast these behaviours with a simple Preisach model that produces return-point memory. Our analysis provides a unified method for comparing and diagnosing cyclic memory behaviours across different materials.
许多处于非平衡态的材料能够“学习”反复施加的一个或多个输入。然而，目前尚缺乏一个理解此类记忆的通用框架。在此，我们将循环记忆行为的最小表示构建为有向图，并构建了能产生相同图结构的简单物理模型。我们展示了公园长椅间磨损草地的模型如何产生多种瞬态记忆——这是一种先前在粒子稀悬浮液和电荷密度波导体中观察到的行为——以及穆林斯效应。在我们的简单模型中分离出这些行为，使我们能够评估此类记忆所需的要素，并量化记忆容量。我们将这些行为与产生回线记忆的简单普雷斯查模型进行对比。我们的分析提供了一种统一的方法，用于比较和诊断不同材料中的循环记忆行为。
