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@ARTICLE{Zajzon:943451,
      author       = {Zajzon, Barna and Dahmen, David and Morrison, Abigail and
                      Duarte, Renato},
      title        = {{S}ignal denoising through topographic modularity of neural
                      circuits},
      journal      = {eLife},
      volume       = {12},
      issn         = {2050-084X},
      address      = {Cambridge},
      publisher    = {eLife Sciences Publications},
      reportid     = {FZJ-2023-01023},
      pages        = {e77009},
      year         = {2023},
      abstract     = {Information from the sensory periphery is conveyed to the
                      cortex via structured projection pathways that spatially
                      segregate stimulus features, providing a robust and
                      efficient encoding strategy. Beyond sensory encoding, this
                      prominent anatomical feature extends throughout the
                      neocortex. However, the extent to which it influences
                      cortical processing is unclear. In this study, we combine
                      cortical circuit modeling with network theory to demonstrate
                      that the sharpness of topographic projections acts as a
                      bifurcation parameter, controlling the macroscopic dynamics
                      and representational precision across a modular network. By
                      shifting the balance of excitation and inhibition,
                      topographic modularity gradually increases task performance
                      and improves the signal-to-noise ratio across the system. We
                      demonstrate that in biologically constrained networks, such
                      a denoising behavior is contingent on recurrent inhibition.
                      We show that this is a robust and generic structural feature
                      that enables a broad range of behaviorally-relevant
                      operating regimes, and provide an in-depth theoretical
                      analysis unravelling the dynamical principles underlying the
                      mechanism.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {600},
      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) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / neuroIC002 - Recurrence and
                      stochasticity for neuro-inspired computation
                      (EXS-SF-neuroIC002) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-82)EXS-SF-neuroIC002 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36700545},
      UT           = {WOS:000943249500001},
      doi          = {10.7554/eLife.77009},
      url          = {https://juser.fz-juelich.de/record/943451},
}