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@ARTICLE{Zajzon:866432,
      author       = {Zajzon, Barna and Mahmoudian, Sepehr and Morrison, Abigail
                      and Duarte, Renato},
      title        = {{P}assing the message: representation transfer in modular
                      balanced networks},
      journal      = {Frontiers in computational neuroscience},
      volume       = {13},
      issn         = {1662-5188},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2019-05574},
      pages        = {79},
      year         = {2019},
      abstract     = {Neurobiological systems rely on hierarchical and modular
                      architectures to carry out intricate computations using
                      minimal resources. A prerequisite for such systems to
                      operate adequately is the capability to reliably and
                      efficiently transfer information across multiple modules.
                      Here, we study the features enabling a robust transfer of
                      stimulus representations in modular networks of spiking
                      neurons, tuned to operate in a balanced regime. To
                      capitalize on the complex, transient dynamics that such
                      networks exhibit during active processing, we apply
                      reservoir computing principles and probe the systems'
                      computational efficacy with specific tasks. Focusing on the
                      comparison of random feed-forward connectivity and
                      biologically inspired topographic maps, we find that, in a
                      sequential set-up, structured projections between the
                      modules are strictly necessary for information to propagate
                      accurately to deeper modules. Such mappings not only improve
                      computational performance and efficiency, they also reduce
                      response variability, increase robustness against
                      interference effects, and boost memory capacity. We further
                      investigate how information from two separate input streams
                      is integrated and demonstrate that it is more advantageous
                      to perform non-linear computations on the input locally,
                      within a given module, and subsequently transfer the result
                      downstream, rather than transferring intermediate
                      information and performing the computation downstream.
                      Depending on how information is integrated early on in the
                      system, the networks achieve similar task-performance using
                      different strategies, indicating that the dimensionality of
                      the neural responses does not necessarily correlate with
                      nonlinear integration, as predicted by previous studies.
                      These findings highlight a key role of topographic maps in
                      supporting fast, robust, and accurate neural communication
                      over longer distances. Given the prevalence of such
                      structural feature, particularly in the sensory systems,
                      elucidating their functional purpose remains an important
                      challenge toward which this work provides relevant, new
                      insights. At the same time, these results shed new light on
                      important requirements for designing functional hierarchical
                      spiking networks.},
      cin          = {INM-6 / IAS-6 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      $I:(DE-82)080012_20140620$},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      EUROSPIN - European Consortium on Synaptic Protein Networks
                      in Neurological and Psychiatric Diseases (241498) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / Functional Neural Architectures
                      $(jinm60_20190501)$},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)241498 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      $G:(DE-Juel1)jinm60_20190501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31920605},
      UT           = {WOS:000503494900001},
      doi          = {10.3389/fncom.2019.00079},
      url          = {https://juser.fz-juelich.de/record/866432},
}