% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Dasbach:901914,
      author       = {Dasbach, Stefan and Tetzlaff, Tom and Diesmann, Markus and
                      Senk, Johanna},
      title        = {{P}rominent characteristics of recurrent neuronal networks
                      are robust against low synaptic weight resolution},
      reportid     = {FZJ-2021-03901, 2105.05002},
      year         = {2021},
      abstract     = {The representation of the natural-density, heterogeneous
                      connectivity of neuronal network models at relevant spatial
                      scales remains a challenge for Computational Neuroscience
                      and Neuromorphic Computing. In particular, the memory
                      demands imposed by the vast number of synapses in
                      brain-scale network simulations constitutes a major
                      obstacle. Limiting the number resolution of synaptic weights
                      appears to be a natural strategy to reduce memory and
                      compute load. In this study, we investigate the effects of a
                      limited synaptic-weight resolution on the dynamics of
                      recurrent spiking neuronal networks resembling local
                      cortical circuits, and develop strategies for minimizing
                      deviations from the dynamics of networks with
                      high-resolution synaptic weights. We mimic the effect of a
                      limited synaptic weight resolution by replacing normally
                      distributed synaptic weights by weights drawn from a
                      discrete distribution, and compare the resulting statistics
                      characterizing firing rates, spike-train irregularity, and
                      correlation coefficients with the reference solution. We
                      show that a naive discretization of synaptic weights
                      generally leads to a distortion of the spike-train
                      statistics. Only if the weights are discretized such that
                      the mean and the variance of the total synaptic input
                      currents are preserved, the firing statistics remains
                      unaffected for the types of networks considered in this
                      study. For networks with sufficiently heterogeneous
                      in-degrees, the firing statistics can be preserved even if
                      all synaptic weights are replaced by the mean of the weight
                      distribution. We conclude that even for simple networks with
                      non-plastic neurons and synapses, a discretization of
                      synaptic weights can lead to substantial deviations in the
                      firing statistics, unless the discretization is performed
                      with care and guided by a rigorous validation process. For
                      the network model used in this study, the synaptic weights
                      can be replaced by low-resolution weights without affecting
                      its macroscopic dynamical characteristics, thereby saving
                      substantial amounts of memory.},
      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          = {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 $(jinb33_20191101)$ / PhD no Grant -
                      Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)SO-092 /
                      $G:(DE-Juel1)jinb33_20191101$ /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)25},
      url          = {https://juser.fz-juelich.de/record/901914},
}