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@ARTICLE{SchultetoBrinke:1014225,
      author       = {Schulte to Brinke, Tobias and Dick, Michael and Duarte,
                      Renato and Morrison, Abigail},
      title        = {{A} refined information processing capacity metric allows
                      an in-depth analysis of memory and nonlinearity trade-offs
                      in neurocomputational systems},
      journal      = {Scientific reports},
      volume       = {13},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2023-03209},
      pages        = {10517},
      year         = {2023},
      abstract     = {Since dynamical systems are an integral part of many
                      scientific domains and can be inherently computational,
                      analyses that reveal in detail the functions they compute
                      can provide the basis for far-reaching advances in various
                      disciplines. One metric that enables such analysis is the
                      information processing capacity. This method not only
                      provides us with information about the complexity of a
                      system’s computations in an interpretable form, but also
                      indicates its different processing modes with different
                      requirements on memory and nonlinearity. In this paper, we
                      provide a guideline for adapting the application of this
                      metric to continuous-time systems in general and spiking
                      neural networks in particular. We investigate ways to
                      operate the networks deterministically to prevent the
                      negative effects of randomness on their capacity. Finally,
                      we present a method to remove the restriction to linearly
                      encoded input signals. This allows the separate analysis of
                      components within complex systems, such as areas within
                      large brain models, without the need to adapt their
                      naturally occurring inputs.},
      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          = {5232 - Computational Principles (POF4-523) / ACA - Advanced
                      Computing Architectures (SO-092) / SDS005 - Towards an
                      integrated data science of complex natural systems
                      (PF-JARA-SDS005) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)SO-092 /
                      G:(DE-Juel-1)PF-JARA-SDS005 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37386240},
      UT           = {WOS:001022752100016},
      doi          = {10.1038/s41598-023-37604-0},
      url          = {https://juser.fz-juelich.de/record/1014225},
}