Cognitive Graph Representation of Human Memory

Abstract

This paper proposes a novel approach to modeling human memory by representing it as a dynamic cognitive graph. The core idea is to map the complex neural activation patterns observed during memory processes onto a graph structure where nodes represent concepts and edges represent the strength and temporal relationships between them. We leverage neuroimaging data, specifically fMRI, to construct this cognitive graph, effectively translating brain activity into a network representation. The simulation of memory retrieval then involves traversing this graph to reconstruct the original memory state. This biologically-inspired method offers a new perspective on the neural basis of memory retrieval, potentially explaining how the brain efficiently encodes, stores, and retrieves information. The framework presented here aims to provide a more nuanced and dynamic understanding of memory compared to traditional static models. Key elements include the definition of graph nodes and edges, the process of graph construction from neuroimaging data, and the algorithmic framework for simulating memory retrieval. Further research will focus on refining the graph construction process and exploring the impact of different graph parameters on memory retrieval performance.