论文标题
通过活动增强非平衡材料中的关联记忆回忆
Enhancing associative memory recall in non-equilibrium materials through activity
论文作者
论文摘要
联想记忆是一种可调地理记忆的一种形式，可促进许多生物和物理系统中的信息存储和检索。在统计力学模型中，平衡处的联想记忆通过自由能景观中的吸引子盆地表示。在这里，我们使用Hopfield模型（范式模型）来描述关联记忆，以研究非平衡活动对记忆保留和召回的影响。我们将活动引入系统中，因为高斯色的噪声破坏了详细的平衡并将系统远离平衡。我们观察到，在这些非平衡条件下，Hopfield网络的存储容量高于平衡时允许的存储容量。使用分析和数值技术，我们表明熵产生的速率会改变能量景观，并帮助系统访问以前无法访问的记忆区域。
Associative memory, a form of content-addressable memory, facilitates information storage and retrieval in many biological and physical systems. In statistical mechanics models, associative memory at equilibrium is represented through attractor basins in the free energy landscape. Here, we use the Hopfield model, a paradigmatic model to describe associate memory, to investigate the effect of non-equilibrium activity on memory retention and recall. We introduce activity into the system as gaussian-colored noise which breaks detailed balance and forces the system out of equilibrium. We observe that, under these non-equilibrium conditions, the Hopfield network has a higher storage capacity than that allowed at equilibrium. Using analytical and numerical techniques, we show that the rate of entropy production modifies the energy landscape and helps the system to access memory regions which were previously inaccessible.
