At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and autonomous behavioral patterns. They rely on algorithms with a global view, significantly differing from biological neural networks, in which, to simulate information storage and retrieval processes, the limitations of centralized algorithms must be overcome. This study introduces a directed graph model that equips each node with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information storage and modeling and simulation of the brain's memory process. We abstract different storage instances as directed graph paths, transforming the storage of information into the assignment, discrimination, and extraction of different paths. To address writing and reading challenges, each node has a personalized adaptive learning ability. A storage algorithm without a God's eye view is developed, where each node uses its limited neighborhood information to facilitate the extension, formation, solidification, and awakening of directed graph paths, achieving competitive, reciprocal, and sustainable utilization of limited resources. Storage behavior occurs in each node, with adaptive learning behaviors of nodes concretized in a microcircuit centered around a variable resistor, simulating the electrophysiological behavior of neurons. Under the constraints of neurobiology on the anatomy and electrophysiology of biological neural networks, this model offers a plausible explanation for the mechanism of memory realization, providing a comprehensive, system-level experimental validation of the memory trace theory.

翻译：在计算科学与认知科学的交叉领域，图论被用于形式化描述复杂关系与结构。传统图模型常为静态的，缺乏动态与自主的行为模式。它们依赖具有全局视野的算法，这与生物神经网络存在显著差异。为了模拟信息存储与检索过程，必须克服集中式算法的局限性。本研究引入一种有向图模型，为每个节点赋予自适应学习与决策能力，从而促进去中心化的动态信息存储，并对大脑记忆过程进行建模与仿真。我们将不同的存储实例抽象为有向图路径，将信息存储转化为不同路径的分配、判别与提取。为解决写入与读取难题，每个节点具备个性化的自适应学习能力。我们开发了一种无需上帝视角的存储算法，每个节点利用其有限的邻域信息来促进有向图路径的扩展、形成、固化与唤醒，实现对有限资源的竞争性、互利性与可持续性利用。存储行为发生在每个节点中，节点的自适应学习行为在以可变电阻器为核心的微电路中具体实现，模拟神经元的电生理行为。在神经生物学对生物神经网络的解剖学与电生理学约束下，该模型为记忆实现机制提供了合理的解释，并对记忆痕迹理论进行了全面、系统级的实验验证。