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@ARTICLE{Emery:259763,
      author       = {Emery, Brett Addison and Hu, Xin and Khanzada, Shahrukh and
                      Kempermann, Gerd and Amin, Hayder},
      title        = {{H}igh-resolution {CMOS}-based biosensor for assessing
                      hippocampal circuit dynamics in experience-dependent
                      plasticity.},
      journal      = {Biosensors and bioelectronics},
      volume       = {237},
      issn         = {0956-5663},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2023-00797},
      pages        = {115471},
      year         = {2023},
      abstract     = {Experiential richness creates tissue-level changes and
                      synaptic plasticity as patterns emerge from rhythmic
                      spatiotemporal activity of large interconnected neuronal
                      assemblies. Despite numerous experimental and computational
                      approaches at different scales, the precise impact of
                      experience on network-wide computational dynamics remains
                      inaccessible due to the lack of applicable large-scale
                      recording methodology. We here demonstrate a large-scale
                      multi-site biohybrid brain circuity on-CMOS-based biosensor
                      with an unprecedented spatiotemporal resolution of 4096
                      microelectrodes, which allows simultaneous
                      electrophysiological assessment across the entire
                      hippocampal-cortical subnetworks from mice living in an
                      enriched environment (ENR) and standard-housed (SD)
                      conditions. Our platform, empowered with various
                      computational analyses, reveals environmental enrichment's
                      impacts on local and global spatiotemporal neural dynamics,
                      firing synchrony, topological network complexity, and
                      large-scale connectome. Our results delineate the distinct
                      role of prior experience in enhancing multiplexed
                      dimensional coding formed by neuronal ensembles and error
                      tolerance and resilience to random failures compared to
                      standard conditions. The scope and depth of these effects
                      highlight the critical role of high-density, large-scale
                      biosensors to provide a new understanding of the
                      computational dynamics and information processing in
                      multimodal physiological and experience-dependent plasticity
                      conditions and their role in higher brain functions.
                      Knowledge of these large-scale dynamics can inspire the
                      development of biologically plausible computational models
                      and computational artificial intelligence networks and
                      expand the reach of neuromorphic brain-inspired computing
                      into new applications.},
      keywords     = {Mice / Animals / Artificial Intelligence / Biosensing
                      Techniques / Neurons: physiology / Hippocampus / Cerebral
                      Cortex / CMOS-MEAs (Other) / Connectome (Other) / Enriched
                      environment (Other) / Graph theory (Other) / Large-scale
                      biosensors (Other) / Neural circuit (Other)},
      cin          = {AG Amin / AG Kempermann},
      ddc          = {610},
      cid          = {I:(DE-2719)1710010 / I:(DE-2719)1710001},
      pnm          = {351 - Brain Function (POF4-351) / 352 - Disease Mechanisms
                      (POF4-352)},
      pid          = {G:(DE-HGF)POF4-351 / G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37379793},
      doi          = {10.1016/j.bios.2023.115471},
      url          = {https://pub.dzne.de/record/259763},
}