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@ARTICLE{Letzkus:285919,
      author       = {Letzkus, Johannes J and Sprekeler, Henning and Binder,
                      Harald and Bödecker, Joschka and Diester, Ilka and Elgueta,
                      Claudio and Grosser, Sabine and Haberl, Matthias and Kulik,
                      Akos and Larkum, Matthew and Leibold, Christian and Madry,
                      Christian and Monyer, Hannah and Poulet, James F A and
                      Sauer, Jonas-Frederic and Schmoranzer, Jan and Schreiber,
                      Susanne and Veit, Julia and Viana da Silva, Silvia and
                      Vlachos, Andreas and Vida, Imre and Geiger, Jörg R P and
                      Bartos, Marlene},
      title        = {{A} population approach to cortical {GABA}ergic interneuron
                      function.},
      journal      = {Neuron},
      volume       = {114},
      number       = {7},
      issn         = {0896-6273},
      address      = {[Cambridge, Mass.]},
      publisher    = {Cell Press},
      reportid     = {DZNE-2026-00365},
      pages        = {1176 - 1180},
      year         = {2026},
      abstract     = {Inhibitory interneuron diversity is a central feature of
                      cortical circuits. The IN-CODE consortium seeks to combine
                      large-scale recordings of interneuron types with
                      machine-learning tools to identify the role of their
                      physiological features, connectivity motifs, and
                      cooperativity in cognitive functions.},
      keywords     = {Interneurons: physiology / GABAergic Neurons: physiology /
                      Animals / Cerebral Cortex: physiology / Cerebral Cortex:
                      cytology / Humans / Machine Learning},
      cin          = {AG Viana-da-Silva},
      ddc          = {610},
      cid          = {I:(DE-2719)5000068},
      pnm          = {351 - Brain Function (POF4-351)},
      pid          = {G:(DE-HGF)POF4-351},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41844157},
      doi          = {10.1016/j.neuron.2026.01.028},
      url          = {https://pub.dzne.de/record/285919},
}