Topological structure determines integration quality and retrieval efficiency

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Abstract

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.

Keywords

synaptic network / knowledge network / retrieval efficiency / integration quality

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References

[1] E. Kandel, Y. Dudai, M. Mayford, The molecular and systems biology of memory, Cell, 157 (2014), 163-186. https://doi.org/10.1016/j.cell.2014.03.001

[2] S. Josselyn, S. Tonegawa, Memory engrams: Recalling the past and imagining the future, Science, 367 (2020), eaaw4325. https://doi.org/10.1126/science.aaw4325

[3] S. Chen, N. Cheng, X. Chen, C. Wang, Integration and competition between space and time in the hippocampus, Neuron, 112 (2024), 3651-3664. https://doi.org/10.1016/j.neuron.2024.08.007

[4] L. D. Kolibius, S. A. Josselyn, S. Hanslmayr, On the origin of memory neurons in the human hippocampus, Trends Cognit. Sci., 29 (2025), 421-433. https://doi.org/10.1016/j.tics.2025.01.013

[5] A. Tzilivaki, J. J. Tukker, N. Maier, P. Poirazi, R. P. Sammons, D. Schmitz, Hippocampal GABAergic interneurons and memory, Neuron, 111 (2023), 3154-3175. https://doi.org/10.1016/j.neuron.2023.06.016

[6] R. E. Harvey, H. L. Robinson, C. Liu, A. Oliva, A. Fernandez-Ruiz, Hippocampo-cortical circuits for selective memory encoding, routing, and replay, Neuron, 111 (2023), 2076-2090. https://doi.org/10.1016/j.neuron.2023.04.015

[7] J. W. Chen, C. Zhang, P. Y. Hu, B. Min, L. P. Wang, Flexible control of sequence working memory in the macaque frontal cortex, Neuron, 112 (2024), 3502-3514. https://doi.org/10.1016/j.neuron.2024.07.024

[8] D. F. Tomé, Y. Zhang, T. Aida, O. Mosto, Y. Lu, M. Chen, et al., Dynamic and selective engrams emerge with memory consolidation, Nat. Neurosci., 27 (2024), 561-572. https://doi.org/10.1038/s41593-023-01551-w

[9] Y. M. Zhou, H. W. Zhu, Z. Y. Liu, X. Chen, X. J. Su, C. N. Ma, et al., A ventral CA1 to nucleus accumbens core engram circuit mediates conditioned place preference for cocaine, Nat. Neurosci., 22 (2019), 1986-1999. https://doi.org/10.1038/s41593-019-0524-y

[10] R. Refaeli, T. Kreisel, M. Groysman, A. Adamsky, I. Goshen, Engram stability and maturation during systems consolidation, Curr. Biol., 33 (2023), 3942-3950. https://doi.org/10.1016/j.cub.2023.07.042

[11] L. Y. Prince, T. J. Bacon, C. M. Tigaret, J. R. Mellor, Neuromodulation of the feedforward dentate gyrus-CA3 microcircuit, Front. Synaptic Neurosci., 8 (2016), 32. https://doi.org/10.3389/fnsyn.2016.00032

[12] B. Lei, B. Kang, Y. Hao, H. Yang, Z. Zhong, Z. Zhai, et al., Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating, Neuron, 113 (2025), 471-485. https://doi.org/10.1016/j.neuron.2024.11.010

[13] D. Osorio-Gómez, M. I. Miranda, K. Guzmán-Ramos, F. Bermúdez-Rattoni, Transforming experiences: Neurobiology of memory updating/editing, Front. Syst. Neurosci., 17 (2023), 1103770. https://doi.org/10.3389/fnsys.2023.1103770

[14] C. Wang, H. Yue, Z. Hu, Y. Shen, J. Ma, J. Li, et al., Microglia mediate forgetting via complement-dependent synaptic elimination, Science, 367 (2020), 688-694. https://doi.org/10.1126/science.aaz2288

[15] J. Ruzicka, M. Dalecka, K. Safrankova, D. Peretti, P. Jendelova, J. C. F. Kwok, et al., Perineuronal nets affect memory and learning after synapse withdrawal, Transl. Psychiatry, 12 (2022), 480. https://doi.org/10.1038/s41398-022-02226-z

[16] N. J. Mandelberg, R. Tsien, Weakening synapses to cull memories, Science, 363 (2019), 31-32. https://doi.org/10.1126/science.aaw1675

[17] R. Fukaya, R. Miyano, H. Hirai, T. Sakaba, Mechanistic insights into cAMP-mediated presynaptic potentiation at hippocampal mossy fiber synapses, Front. Cell. Neurosci., 17 (2023), 1237589. https://doi.org/10.3389/fncel.2023.1237589

[18] K. Isler, C. P. van Schaik, Metabolic costs of brain size evolution, Biol. Lett., 2 (2006), 557-560. https://doi.org/10.1098/rsbl.2006.0538

[19] E. Bullmore, O. Sporns, The economy of brain network organization, Nat. Rev. Neurosci., 13 (2012), 336-349. https://doi.org/10.1038/nrn3214

[20] Q. She, G. Chen, R. Chan, Evaluating the small-world-ness of a sampled network: Functional connectivity of entorhinal-hippocampal circuitry, Sci. Rep., 6 (2016), 21468. https://doi.org/10.1038/srep21468

[21] S. Muldoon, E. Bridgeford, D. Bassett, Small-world propensity and weighted brain networks, Sci. Rep., 6 (2016), 22057. https://doi.org/10.1038/srep22057

[22] P. Taylor, Y. Wang, M. Kaiser, Within brain area tractography suggests local modularity using high resolution connectomics, Sci. Rep., 7 (2017), 39859. https://doi.org/10.1038/srep39859

[23] M. P. van den Heuvel, O. Sporns, Network hubs in the human brain, Trends Cognit. Sci., 17 (2013), 683-696. https://doi.org/10.1016/j.tics.2013.09.012

[24] O. Sporns, Structure and function of complex brain networks, Dialogues Clin. Neurosci., 15 (2013), 247-262. https://doi.org/10.31887/DCNS.2013.15.3/osporns

[25] A. Hahn, M. Breakspear, L. Rischka, W. Wadsak, G. M. Godbersen, V. Pichler, et al., Reconfiguration of functional brain networks and metabolic cost converge during task performance, eLife, 9 (2020), e52443. https://doi.org/10.7554/eLife.52443

[26] L. K. Gallos, H. A. Makse, M. Sigman, A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 2825-2830. https://doi.org/10.1073/pnas.1106612109

[27] L. Yang, H. H. Zhang, Z. K. Sun, L. Du, G. R. Chen, Hippocampal CA3-CA1 synaptic network model of memory, Nonlinear Dyn., 112 (2024), 7499-7525. https://doi.org/10.1007/s11071-024-09375-4

[28] L. Yang, H. H. Zhang, Z. K. Sun, Hippocampus encoding memory engrams as stable heteroclinic network, Chaos, 34 (2024), 123118. https://doi.org/10.1063/5.0223045

[29] L. Yang, H. H. Zhang, Z. K. Sun, Modeling dynamics of memory integration and retrieval in the hippocampus, Eur. Phys. J. Spec. Top., 2025 (2025). https://doi.org/10.1140/epjs/s11734-025-01821-7

[30] T. D. Shou, Neurobiology, 3nd edition (in Chinese) , Beijing: Higher Education Press, 2013.

[31] A. Guet-McCreight, F. K. Skinner, L. Topolnik, Common principles in functional organization of VIP/calretinin cell-driven disinhibitory circuits across cortical areas, Front. Neural Circuits, 14 (2020), 32. https://doi.org/10.3389/fncir.2020.00032

[32] E. Abs, R. B. Poorthuis, D. Apelblat, K. Muhammad, M. B. Pardi, L. Enke, et al., Learning-related plasticity in dendrite-targeting layer 1 interneurons, Neuron, 100 (2018), 684-699. https://doi.org/10.1016/j.neuron.2018.09.001

[33] Z. Kohus, S. Káli, L. Rovira-Esteban, D. Schlingloff, O. Papp, T. F. Freund, et al., Properties and dynamics of inhibitory synaptic communication within the CA3 microcircuits of pyramidal cells and interneurons expressing parvalbumin or cholecystokinin, J. Physiol., 594 (2016), 3745-3774. https://doi.org/10.1113/JP272231

[34] R. Evangelista, G. Cano, C. Cooper, D. Schmitz, N. Maier, R. Kempter, Generation of sharp wave-ripple events by disinhibition, J. Neurosci., 40 (2020), 7811-7836. https://doi.org/10.1523/JNEUROSCI.2174-19.2020

[35] B. Dudok, P. M. Klein, E. Hwaun, B. R. Lee, Z. Yao, O. Fong, et al., Alternating sources of perisomatic inhibition during behavior, Neuron, 109 (2021), 997-1012. https://doi.org/10.1016/j.neuron.2021.01.003

[36] M. Udakis, V. Pedrosa, S. E. L. Chamberlain, C. Clopath, J. R. Mellor, Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output, Nat. Commun., 11 (2020), 4395. https://doi.org/10.1038/s41467-020-18074-8

[37] T. Jing, L. Yang, W. G. Sun, Exact calculations of network coherence in weighted ring-trees networks and recursive trees, Phys. Scr., 96 (2021), 085217. https://doi.org/10.1088/1402-4896/ac0277

[38] J. Chen, L. Zhou, W. G. Sun, Consensus analysis of chain star networks coupled by leaf nodes, Phys. Scr., 98 (2023), 125204. https://doi.org/10.1088/1402-4896/ad0588

[39] A. J. DeCostanzo, C. C. A. Fung, T. Fukai, Hippocampal neurogenesis reduces the dimensionality of sparsely coded representations to enhance memory encoding, Front. Comput. Neurosci., 12 (2019), 99. https://doi.org/10.3389/fncom.2018.00099

[40] P. Rao-Ruiz, E. Visser, M. Mitrić, A. B. Smit, M. C. van den Oever, A synaptic framework for the persistence of memory engrams, Front. Synaptic Neurosci., 13 (2021), 661476. https://doi.org/10.3389/fnsyn.2021.661476

[41] K. M. Swallow, J. M. Zacks, R. A. Abrams, Event boundaries in perception affect memory encoding and updating, J. Exp. Psychol. Gen., 138 (2009), 236-257. https://doi.org/10.1037/a0015631

[42] S. Tazerart, D. E. Mitchell, S. Miranda-Rottmann, R. Araya, A spike-timing-dependent plasticity rule for dendritic spines, Nat. Commun., 11 (2020), 4276. https://doi.org/10.1038/s41467-020-17861-7