  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.
