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@ARTICLE{Jordan:866396,
      author       = {Jordan, Jakob and Petrovici, Mihai A. and Breitwieser,
                      Oliver and Schemmel, Johannes and Meier, Karlheinz and
                      Diesmann, Markus and Tetzlaff, Tom},
      title        = {{D}eterministic networks for probabilistic computing},
      journal      = {Scientific reports},
      volume       = {9},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2019-05550},
      pages        = {18303},
      year         = {2019},
      abstract     = {Neuronal network models of high-level brain functions such
                      as memory recall and reasoning often rely on the presence of
                      some form of noise. The majority of these models assumes
                      that each neuron in the functional network is equipped with
                      its own private source of randomness, often in the form of
                      uncorrelated external noise. In vivo, synaptic background
                      input has been suggested to serve as the main source of
                      noise in biological neuronal networks. However, the
                      finiteness of the number of such noise sources constitutes a
                      challenge to this idea. Here, we show that shared-noise
                      correlations resulting from a finite number of independent
                      noise sources can substantially impair the performance of
                      stochastic network models. We demonstrate that this problem
                      is naturally overcome by replacing the ensemble of
                      independent noise sources by a deterministic recurrent
                      neuronal network. By virtue of inhibitory feedback, such
                      networks can generate small residual spatial correlations in
                      their activity which, counter to intuition, suppress the
                      detrimental effect of shared input. We exploit this
                      mechanism to show that a single recurrent network of a few
                      hundred neurons can serve as a natural noise source for a
                      large ensemble of functional networks performing
                      probabilistic computations, each comprising thousands of
                      units.},
      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          = {574 - Theory, modelling and simulation (POF3-574) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / Advanced Computing Architectures
                      $(aca_20190115)$ / HBP - The Human Brain Project (604102) /
                      HBP SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270) / BRAINSCALES - Brain-inspired multiscale
                      computation in neuromorphic hybrid systems (269921)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)604102 /
                      G:(EU-Grant)720270 / G:(EU-Grant)269921},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31797943},
      UT           = {WOS:000501433500001},
      doi          = {10.1038/s41598-019-54137-7},
      url          = {https://juser.fz-juelich.de/record/866396},
}