A Computational Framework for Learning and Memory: Network Motif Evolution During LTP-Induced Plasticity.

Hu, Xin; Amin, Hayder; Emery, Brett Addison; Khanzada, Shahrukh

doi:10.1109/EMBC58623.2025.11254218

Contribution to a conference proceedings/Contribution to a book

DZNE-2026-00343

A Computational Framework for Learning and Memory: Network Motif Evolution During LTP-Induced Plasticity.

Hu, X. (First author)DZNE* ; Khanzada, S.DZNE* ; Emery, B. A.DZNE* ; Amin, H. (Last author)DZNE*

2025
IEEE

2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) : [Proceedings] - IEEE, 2025. - ISBN 979-8-3315-8618-8 - doi:10.1109/EMBC58623.2025.11254218
47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, Copenhagen, Denmark, 14 Jul 2025 - 18 Jul 2025 IEEE 1-4 (2025) [10.1109/EMBC58623.2025.11254218]

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Abstract: Unraveling the complexity of network-level synaptic plasticity remains a challenge due to the dynamic and interconnected nature of neural circuits. In this study, we employ network motifs-recurrent, functionally specialized patterns of connectivity-as a framework to dissect long-term potentiation (LTP)-induced reorganization in hippocampal CA1-CA3 networks. Using network LTP recordings from highdensity microelectrode arrays (HD-MEA), we systematically tracked motif evolution before and after high-frequency stimulation, assessing their roles in network stability, synaptic strength modulation, and criticality-redundancy trade-offs, linking these dynamics to firing synchrony and network potentiation. The Graph-theoretic analysis further demonstrated that LTP-induced reorganization follows a structured motif-guided trajectory, with early-phase motif recruitment optimizing efficiency, followed by phase-dependent refinement. These findings provide new insights into how structured connectivity enables network-level plasticity, balancing efficiency with stability, and offer a potential framework for understanding memory encoding mechanisms and their dysfunction in neurological disorders.

Keyword(s): Long-Term Potentiation: physiology (MeSH) ; Animals (MeSH) ; Memory: physiology (MeSH) ; Neuronal Plasticity: physiology (MeSH) ; Learning: physiology (MeSH) ; Models, Neurological (MeSH) ; Rats (MeSH) ; Nerve Net: physiology (MeSH)