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@INPROCEEDINGS{Hu:285807,
      author       = {Hu, Xin and Khanzada, Shahrukh and Emery, Brett Addison and
                      Amin, Hayder},
      title        = {{A} {C}omputational {F}ramework for {L}earning and
                      {M}emory: {N}etwork {M}otif {E}volution {D}uring
                      {LTP}-{I}nduced {P}lasticity.},
      publisher    = {IEEE},
      reportid     = {DZNE-2026-00343},
      pages        = {1-4},
      year         = {2025},
      note         = {Missing Journal: Annu Int Conf IEEE Eng Med Biol Soc =
                      2375-7477 (import from CrossRef Conference, PubMed, ,
                      Journals: pub.dzne.de)},
      comment      = {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},
      booktitle     = {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},
      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.},
      month         = {Jul},
      date          = {2025-07-14},
      organization  = {47th Annual International Conference
                       of the IEEE Engineering in Medicine and
                       Biology Society, Copenhagen (Denmark),
                       14 Jul 2025 - 18 Jul 2025},
      keywords     = {Long-Term Potentiation: physiology / Animals / Memory:
                      physiology / Neuronal Plasticity: physiology / Learning:
                      physiology / Models, Neurological / Rats / Nerve Net:
                      physiology},
      cin          = {AG Amin},
      cid          = {I:(DE-2719)1710010},
      pnm          = {351 - Brain Function (POF4-351)},
      pid          = {G:(DE-HGF)POF4-351},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      pubmed       = {pmid:41336996},
      doi          = {10.1109/EMBC58623.2025.11254218},
      url          = {https://pub.dzne.de/record/285807},
}