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@INPROCEEDINGS{SchultetoBrinke:889655,
      author       = {Schulte to Brinke, Tobias and Khalid, Fahad and Duarte,
                      Renato and Morrison, Abigail},
      title        = {{P}rocessing capacity of recurrent spiking networks},
      reportid     = {FZJ-2021-00287},
      year         = {2020},
      abstract     = {One of the most prevalent characteristics of
                      neurobiological systems is the abundance of recurrent
                      connectivity. Regardless of the spatial scale considered,
                      recurrence is a fundamental design principle and a core
                      anatomical feature, permeating the micro-, meso- and
                      macroscopic levels. In essence, the brain (and, in
                      particular, the mammalian neocortex) can be seen as a large
                      recurrent network of recurrent networks. Despite the
                      ubiquity of these observations, it remains unclear whether
                      recurrence and the characteristics of its biophysical
                      properties correspond to important functional
                      specializations and if so, to what extent. ​ Intuitively,
                      from a computational perspective, recurrence allows
                      information to be propagated in time, i.e. past information
                      reverberates so as to influence online processing, endowing
                      the circuits with memory and sensitivity to temporal
                      structure. However, even in its simpler formulations, the
                      functional relevance and computational consequences of
                      recurrence in biophysical models of spiking networks are not
                      clear or unambiguous and its effects vary depending on the
                      type and characteristics of the system under analysis and
                      the nature of the computational task. Therefore, it would be
                      extremely useful, from both an engineering and a
                      neurobiological perspective, to know to what extent is
                      recurrence necessary for neural computation. ​ In this
                      work, we set out to quantify the extent to which recurrence
                      modulates a circuit's computational capacity, by
                      systematically measuring its ability to perform arbitrary
                      transformations on an input, following [1]. By varying the
                      strength and density of recurrent connections in balanced
                      networks of spiking neurons, we evaluate the effect of
                      recurrence on the complexity of the transformations the
                      circuit can carry out and on the memory it is able to
                      sustain. Preliminary results demonstrates some constraints
                      on recurrent connectivity that optimize its processing
                      capabilities for mappings that involve both linear memory
                      and varying degrees of nonlinearity. ​ Additionally, given
                      that the metric we employ is particularly computationally-
                      heavy (evaluating the system's capacity to represent
                      thousands of target functions), a careful optimization and
                      parallelization strategy is employed, enabling its
                      application to networks of neuroscientific interest. We
                      present a highly scalable and computationally efficient
                      software, which pre-computes the thousands of necessary
                      target polynomial functions for each point in a large
                      combinatorial space, accesses these target functions through
                      an efficient lookup operation, caches functions that need to
                      be called multiple times with the same inputs and optimizes
                      the most compute-intensive hotspots with Cython. In
                      combination with MPI for internode communication this
                      results in a highly scalable and computationally efficient
                      implementation to determine the processing capacity of a
                      dynamical system.References[1] Dambre J, Verstraeten D,
                      Schrauwen B, Massar S. Information Processing Capacity of
                      Dynamical Systems. Sci Rep. 2012, 2. 514.},
      month         = {Jul},
      date          = {2020-07-18},
      organization  = {Organisation For Computational
                       Neuroscience - 29th Annual
                       Computational Neuroscience Meeting,
                       Online (Online), 18 Jul 2020 - 23 Jul
                       2020},
      subtyp        = {After Call},
      cin          = {INM-6 / JSC},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)JSC-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 511 -
                      Computational Science and Mathematical Methods (POF3-511) /
                      PhD no Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405) / Advanced Computing Architectures
                      $(aca_20190115)$ / SMHB - Supercomputing and Modelling for
                      the Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-511 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405 /
                      $G:(DE-Juel1)aca_20190115$ / G:(DE-Juel1)HGF-SMHB-2013-2017
                      / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/889655},
}