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@ARTICLE{SchultetoBrinke:916126,
      author       = {Schulte to Brinke, Tobias and Duarte, Renato and Morrison,
                      Abigail},
      title        = {{C}haracteristic columnar connectivity caters to cortical
                      computation: {R}eplication, simulation, and evaluation of a
                      microcircuit model},
      journal      = {Frontiers in integrative neuroscience},
      volume       = {16},
      issn         = {1662-5145},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2022-05957},
      pages        = {923468},
      year         = {2022},
      abstract     = {The neocortex, and with it the mammalian brain, achieves a
                      level of computational efficiency like no other existing
                      computational engine. A deeper understanding of its building
                      blocks (cortical microcircuits), and their underlying
                      computational principles is thus of paramount interest. To
                      this end, we need reproducible computational models that can
                      be analyzed, modified, extended and quantitatively compared.
                      In this study, we further that aim by providing a
                      replication of a seminal cortical column model. This model
                      consists of noisy Hodgkin-Huxley neurons connected by
                      dynamic synapses, whose connectivity scheme is based on
                      empirical findings from intracellular recordings. Our
                      analysis confirms the key original finding that the
                      specific, data-based connectivity structure enhances the
                      computational performance compared to a variety of
                      alternatively structured control circuits. For this
                      comparison, we use tasks based on spike patterns and rates
                      that require the systems not only to have simple
                      classification capabilities, but also to retain information
                      over time and to be able to compute nonlinear functions.
                      Going beyond the scope of the original study, we demonstrate
                      that this finding is independent of the complexity of the
                      neuron model, which further strengthens the argument that it
                      is the connectivity which is crucial. Finally, a detailed
                      analysis of the memory capabilities of the circuits reveals
                      a stereotypical memory profile common across all circuit
                      variants. Notably, the circuit with laminar structure does
                      not retain stimulus any longer than any other circuit type.
                      We therefore conclude that the model's computational
                      advantage lies in a sharper representation of the stimuli.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / ACA - Advanced
                      Computing Architectures (SO-092) /
                      Open-Access-Publikationskosten Forschungszentrum Jülich
                      (OAPKFZJ) (491111487) / SDS005 - Towards an integrated data
                      science of complex natural systems (PF-JARA-SDS005)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)SO-092 /
                      G:(GEPRIS)491111487 / G:(DE-Juel-1)PF-JARA-SDS005},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36310713},
      UT           = {WOS:000876845600001},
      doi          = {10.3389/fnint.2022.923468},
      url          = {https://juser.fz-juelich.de/record/916126},
}