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.
Short-term plasticity recalls forgotten memories through a trampoline mechanism
Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；Department of Applied Physics, Stanford University（斯坦福大学应用物理系）
Electrolyte Bonding Engineering for Highly Uniform GeTe-based CBRAM and Parallel Hebbian Learning in Selector-free Hopfield Networks
Center for Semiconductor Technology, Korea Institute of Science and Technology（韩国科学技术院半导体技术中心）；Nanoscience and Technology, KIST School, University of Science and Technology（韩国科学技术院KIST学院纳米科学与技术）；Department of Materials Science and Engineering, Seoul National University（首尔大学材料科学与工程系）；Department of Physics and Astronomy, Seoul National University（首尔大学物理与天文学系）；Department of Electrical Engineering, Korea University（高丽大学电气工程系）；Department of Materials Science and Engineering, Korea University（高丽大学材料科学与工程系）
