Dynamic Graph for Biological Memory Modeling: A System-Level Validation

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a novel dynamic directed graph model that simulates the brain’s memory process by empowering each node with adaptive learning and decision-making capabilities. This decentralized approach transforms memory storage into the management of directed graph paths, with each node utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model’s unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Each node’s adaptive learning behavior is represented through a microcircuit centered around a variable resistor. We validated the model’s efficacy in storing and retrieving data through computer simulations. This approach offers a plausible biological explanation for memory realization and validates the memory trace theory at a system level.