Topological structure determines integration quality and retrieval efficiency

The hippocampus in the brain encodes memorized information as long-term memory through the plasticity of the CA3–CA1 synaptic network, thereby forming stable engrams. Based on the hippocampal CA3–CA1 synaptic network model, memory engrams are encoded as trajectories within a stable heteroclinic network, where saddle points signify different information blocks. This stable heteroclinic network is further refined into a knowledge network that illustrates the relationships between information blocks, defining the retrieval efficiency, capability, and integration quality of the network among these blocks. In this paper, we examined the impact of the local and global topological properties of the knowledge network on retrieval efficiency, capability, and integration quality. Numerical results indicated that the retrieval efficiency between any two information blocks in the knowledge network decreased with the out-degree of the cue’s corresponding node, increased with the in-degree of the target’s corresponding node, and was negatively correlated with the distance between node pairs. The retrieval capability of any information block was determined by the out-degree centrality and out-closeness of the corresponding node, while the integration quality of the knowledge network was influenced by the information blocks corresponding to nodes with higher degree centrality or betweenness centrality, and was negatively correlated with the average path length of the network.