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：在计算与认知科学的交叉领域，图论被用作复杂关系与结构的形式化描述工具。传统图模型通常是静态的，缺乏动态自主的行为模式，且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。本研究提出一种有向图模型，赋予每个节点自适应学习与决策能力，从而实现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。我们开发了一种无上帝视角的存储算法：每个节点利用其有限的邻域信息促进有向图路径的延伸、形成、固化与唤醒，实现有限资源的竞争性、互惠性与可持续利用。存储行为发生于每个节点内部，节点的自适应学习行为通过以可变电阻为核心的微电路具体实现，模拟神经元的电生理行为。在神经生物学对生物神经网络解剖结构与电生理特性的约束下，该模型为记忆实现机制提供了合理解释，为记忆痕迹理论提供了系统级的全面实验验证。