%0 Conference Paper
%A Hu, Xin
%A Khanzada, Shahrukh
%A Emery, Brett Addison
%A Amin, Hayder
%T A Computational Framework for Learning and Memory: Network Motif Evolution During LTP-Induced Plasticity.
%I IEEE
%M DZNE-2026-00343
%P 1-4
%D 2025
%Z Missing Journal: Annu Int Conf IEEE Eng Med Biol Soc = 2375-7477 (import from CrossRef Conference, PubMed, , Journals: pub.dzne.de)
%< 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
%X 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.
%B 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
%C 14 Jul 2025 - 18 Jul 2025, Copenhagen (Denmark)
Y2 14 Jul 2025 - 18 Jul 2025
M2 Copenhagen, Denmark
%K Long-Term Potentiation: physiology
%K Animals
%K Memory: physiology
%K Neuronal Plasticity: physiology
%K Learning: physiology
%K Models, Neurological
%K Rats
%K Nerve Net: physiology
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%$ pmid:41336996
%R 10.1109/EMBC58623.2025.11254218
%U https://pub.dzne.de/record/285807