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| 037 | _ | _ | |a DZNE-2026-00343 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Hu, Xin |0 P:(DE-2719)2814182 |b 0 |e First author |u dzne |
| 111 | 2 | _ | |a 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |g EMBC |c Copenhagen |d 2025-07-14 - 2025-07-18 |w Denmark |
| 245 | _ | _ | |a A Computational Framework for Learning and Memory: Network Motif Evolution During LTP-Induced Plasticity. |
| 260 | _ | _ | |c 2025 |b IEEE |
| 295 | 1 | 0 | |a 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 |
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| 500 | _ | _ | |a Missing Journal: Annu Int Conf IEEE Eng Med Biol Soc = 2375-7477 (import from CrossRef Conference, PubMed, , Journals: pub.dzne.de) |
| 520 | _ | _ | |a 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. |
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| 650 | _ | 2 | |a Long-Term Potentiation: physiology |2 MeSH |
| 650 | _ | 2 | |a Animals |2 MeSH |
| 650 | _ | 2 | |a Memory: physiology |2 MeSH |
| 650 | _ | 2 | |a Neuronal Plasticity: physiology |2 MeSH |
| 650 | _ | 2 | |a Learning: physiology |2 MeSH |
| 650 | _ | 2 | |a Models, Neurological |2 MeSH |
| 650 | _ | 2 | |a Rats |2 MeSH |
| 650 | _ | 2 | |a Nerve Net: physiology |2 MeSH |
| 700 | 1 | _ | |a Khanzada, Shahrukh |0 P:(DE-2719)9001867 |b 1 |u dzne |
| 700 | 1 | _ | |a Emery, Brett Addison |0 P:(DE-2719)9001361 |b 2 |u dzne |
| 700 | 1 | _ | |a Amin, Hayder |0 P:(DE-2719)2812628 |b 3 |e Last author |u dzne |
| 773 | _ | _ | |a 10.1109/EMBC58623.2025.11254218 |
| 856 | 4 | _ | |u https://pub.dzne.de/record/285807/files/DZNE-2026-00343_Restricted.pdf |
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