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@ARTICLE{Dasbach:905087,
      author       = {Dasbach, Stefan and Tetzlaff, Tom and Diesmann, Markus and
                      Senk, Johanna},
      title        = {{D}ynamical {C}haracteristics of {R}ecurrent {N}euronal
                      {N}etworks {A}re {R}obust {A}gainst {L}ow {S}ynaptic
                      {W}eight {R}esolution},
      journal      = {Frontiers in neuroscience},
      volume       = {15},
      issn         = {1662-453X},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2022-00386},
      pages        = {757790},
      year         = {2021},
      abstract     = {The representation of the natural-density, heterogeneous
                      connectivity of neuronalnetwork models at relevant spatial
                      scales remains a challenge for ComputationalNeuroscience and
                      Neuromorphic Computing. In particular, the memory
                      demandsimposed by the vast number of synapses in brain-scale
                      network simulations constitutea major obstacle. Limiting the
                      number resolution of synaptic weights appears to bea natural
                      strategy to reduce memory and compute load. In this study,
                      we investigatethe effects of a limited synaptic-weight
                      resolution on the dynamics of recurrent spikingneuronal
                      networks resembling local cortical circuits and develop
                      strategies for minimizingdeviations from the dynamics of
                      networks with high-resolution synaptic weights. Wemimic the
                      effect of a limited synaptic weight resolution by replacing
                      normally distributedsynaptic weights with weights drawn from
                      a discrete distribution, and compare theresulting statistics
                      characterizing firing rates, spike-train irregularity, and
                      correlationcoefficients with the reference solution. We show
                      that a naive discretization of synapticweights generally
                      leads to a distortion of the spike-train statistics. If the
                      weights arediscretized such that the mean and the variance
                      of the total synaptic input currents arepreserved, the
                      firing statistics remain unaffected for the types of
                      networks considered inthis study. For networks with
                      sufficiently heterogeneous in-degrees, the firing
                      statisticscan be preserved even if all synaptic weights are
                      replaced by the mean of the weightdistribution. We conclude
                      that even for simple networks with non-plastic neurons
                      andsynapses, a discretization of synaptic weights can lead
                      to substantial deviations in thefiring statistics unless the
                      discretization is performed with care and guided by a
                      rigorousvalidation process. For the network model used in
                      this study, the synaptic weightscan be replaced by
                      low-resolution weights without affecting its macroscopic
                      dynamicalcharacteristics, thereby saving substantial amounts
                      of memory.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / ACA - Advanced Computing Architectures (SO-092) /
                      Brain-Scale Simulations $(jinb33_20191101)$},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)SO-092 /
                      $G:(DE-Juel1)jinb33_20191101$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35002599},
      UT           = {WOS:000743979800001},
      doi          = {10.3389/fnins.2021.757790},
      url          = {https://juser.fz-juelich.de/record/905087},
}