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@INPROCEEDINGS{Zajzon:894276,
      author       = {Zajzon, Barna and Dahmen, David and Morrison, Abigail and
                      Duarte, Renato},
      title        = {{S}ignal denoising through modular topography},
      reportid     = {FZJ-2021-03149},
      year         = {2021},
      abstract     = {To navigate in a dynamic and noisy environment, the brain
                      must create reliable and meaningful representations from
                      sensory inputs that are often ambiguous, incomplete or
                      corrupt. From these noisy inputs, cortical circuits extract
                      the relevant features to forge a ground truth against which
                      internally generated signals from inferential processes can
                      be evaluated. Since information that fails to permeate the
                      cortical hierarchy can not influence sensory perception and
                      decision-making, it is critical that external stimuli are
                      encoded and propagated through different processing stages
                      in a manner that minimizes signal degradation.In this study,
                      we hypothesize that stimulus-specific pathways akin to
                      cortical topographic maps may provide the structural
                      scaffold for such signal routing. A pervasive structural
                      feature of the mammalian neocortex, topographic projections
                      can imprint spatiotemporal features of (noisy) sensory
                      inputs onto the cortex by preserving the relative
                      organization of cells between distinct populations. Here, we
                      investigate whether the feature-specific pathways within
                      such maps can guide and route stimulus information
                      throughout the system while retaining representational
                      fidelity.We demonstrate that, in a large modular circuit of
                      spiking neurons comprising multiple sub-networks,
                      topographic projections can help the system reduce sensory
                      and intrinsic noise to enable an accurate propagation of
                      stimulus representations. Moreover, by regulating the
                      effective connectivity and local E/I balance, modular
                      topographic precision can instantiate a de-facto denoising
                      auto-encoder, whereby the system's internal representation
                      is gradually improved and signal-to-noise ratio increased as
                      the input signal is transmitted through the network. Such a
                      denoising function arises beyond a critical transition point
                      in the sharpness of the feed-forward projections, and is
                      characterized by the emergence of inhibition-dominated
                      regimes where population responses along stimulated maps are
                      amplified and others are weakened.In addition, we
                      demonstrate that this is a generalizable and robust
                      structural effect, largely independent of the underlying
                      architectural specificities. Using mean-field
                      approximations, we gain deeper insight into the mechanisms
                      responsible for the qualitative changes in the system's
                      behavior and show that these depend only on the modular
                      topographic connectivity and stimulus intensity. The general
                      dynamical principle revealed by the theoretical predictions
                      suggest that such a denoising property may be a universal,
                      system-agnostic feature of topographic maps. Finally, our
                      results indicate that structured projection patterns can
                      enable a wide range of behaviorally relevant regimes
                      observed under various experimental conditions: maintaining
                      stable representations of multiple stimuli across cortical
                      circuits; amplifying certain features while suppressing
                      others, resembling winner-take-all circuits; and endow
                      circuits with metastable dynamics (winnerless competition),
                      assumed to be fundamental in a variety of tasks.},
      month         = {Jul},
      date          = {2021-07-03},
      organization  = {30th Annual Computational Neuroscience
                       Meeting, Online (Germany), 3 Jul 2021 -
                       7 Jul 2021},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      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)},
      pid          = {G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/894276},
}