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@INPROCEEDINGS{Jordan:155477,
      author       = {Jordan, Jakob and Petrovici, Mihai and Pfeil, Thomas and
                      Breitwieser, Oliver and Bytschok, Ilya and Bill, Johannes
                      and Gruebl, Andreas and Schemmel, Johannes and Meier,
                      Karlheinz and Diesmann, Markus and Tetzlaff, Tom},
      title        = {{N}eural {N}etworks as {S}ources of uncorrelated {N}oise
                      for functional neural {S}ystems},
      reportid     = {FZJ-2014-04643},
      year         = {2014},
      abstract     = {Neural-network models of brain function often rely on the
                      presence of noise [1,2]. Yet thebiological origin of this
                      noise remains unclear. In computer simulations and in
                      neuromorphichardware [3,4], the number of noise sources
                      (random-number generators) is limited. In conse-quence,
                      neurons in large functional network models have to share
                      noise sources and are thereforecorrelated. It is largely
                      unclear how shared-noise correlation affect the performance
                      of func-tional network models. Further, so far there is no
                      solution to the problem of how a limitednumber of noise
                      sources can supply a large number of functional units with
                      uncorrelated noise.Here, we first demonstrate that the
                      performance of two functional network models, attrac-tor
                      networks [5] and neural Boltzmann machines [2], is
                      substantially impaired by shared-noisecorrelations resulting
                      from a limited number of noise sources. Secondly, we show
                      that thisproblem can be overcome by replacing the finite
                      pool of independent noise sources by a (finite)recurrent
                      neural network. As shown recently, inhibitory feedback,
                      abundant in biological neu-ral networks, serves as a
                      powerful decorrelation mechanism [6,7]. Shared-noise
                      correlations areactively suppressed by the network dynamics.
                      By exploiting this effect, the network perfor-mance is
                      significantly improved. Finally, we demonstrate the
                      decorrelating effect of inhibitoryfeedback in a
                      heterogeneous network implemented in an analog neuromorphic
                      substrate [8]. Insummary, we show that recurrent neural
                      networks can serve as natural finite-size noise sourcesfor
                      functional neural networks, both in biological and in
                      synthetic neuromorphic substrates.},
      month         = {May},
      date          = {2014-05-07},
      organization  = {OCCAM 2014, Osnabrueck (Germany), 7
                       May 2014 - 9 May 2014},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / BRAINSCALES - Brain-inspired multiscale
                      computation in neuromorphic hybrid systems (269921) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP - The Human Brain Project
                      (604102) / 89574 - Theory, modelling and simulation
                      (POF2-89574)},
      pid          = {G:(DE-HGF)POF2-331 / G:(EU-Grant)269921 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)604102 /
                      G:(DE-HGF)POF2-89574},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/155477},
}