Modeling of Memory Mechanisms in Cerebral Cortex and Simulation of Storage Performance
Hui Wei, Chenyue Feng, Jianning Zhang
TL;DR
The paper addresses how memory encoding, storage, and retrieval in the cerebral cortex can be modeled without a global controller. It introduces a decentralized, directed-graph memory model where each node autonomously learns and forms storage paths through local interactions, complemented by a circuit-inspired microstructure. Path formation relies on adaptive resource competition and electric-field-driven current flows, with retrieval via probe vectors that can handle partial information and path overlaps through mechanisms like retroactive inhibition. Extensive simulations across varying graph topologies, depths, and activation patterns demonstrate memory-like storage, retrieval, and chain awakening, and benchmark performance against Hopfield networks, highlighting higher capacity in shallower networks and robustness to topology. The work provides a biologically plausible framework for memory trace realization and offers guidance for neuromorphic memory design and neural computation research.
Abstract
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.