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@ARTICLE{Heiberg:851430,
      author       = {Heiberg, Thomas and Kriener, Birgit and Tetzlaff, Tom and
                      Einevoll, Gaute T. and Plesser, Hans E.},
      title        = {{F}iring-rate models for neurons with a broad repertoire of
                      spiking behaviors},
      journal      = {Journal of computational neuroscience},
      volume       = {45},
      number       = {2},
      issn         = {0929-5313},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2018-05077},
      pages        = {103-132},
      year         = {2018},
      abstract     = {Capturing the response behavior of spiking neuron models
                      with rate-based models facilitates the investigation of
                      neuronal networks using powerful methods for rate-based
                      network dynamics. To this end, we investigate the responses
                      of two widely used neuron model types, the Izhikevich and
                      augmented multi-adapative threshold (AMAT) models, to a
                      range of spiking inputs ranging from step responses to
                      natural spike data. We find (i) that linear-nonlinear firing
                      rate models fitted to test data can be used to describe the
                      firing-rate responses of AMAT and Izhikevich spiking neuron
                      models in many cases; (ii) that firing-rate responses are
                      generally too complex to be captured by first-order low-pass
                      filters but require bandpass filters instead; (iii) that
                      linear-nonlinear models capture the response of AMAT models
                      better than of Izhikevich models; (iv) that the wide range
                      of response types evoked by current-injection experiments
                      collapses to few response types when neurons are driven by
                      stationary or sinusoidally modulated Poisson input; and (v)
                      that AMAT and Izhikevich models show different responses to
                      spike input despite identical responses to current
                      injections. Together, these findings suggest that rate-based
                      models of network dynamics may capture a wider range of
                      neuronal response properties by incorporating second-order
                      bandpass filters fitted to responses of spiking model
                      neurons. These models may contribute to bringing rate-based
                      network modeling closer to the reality of biological
                      neuronal networks.},
      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          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA1 - Human Brain Project Specific Grant
                      Agreement 1 (720270) / SMHB - Supercomputing and Modelling
                      for the Human Brain (HGF-SMHB-2013-2017) / BRAINSCALES -
                      Brain-inspired multiscale computation in neuromorphic hybrid
                      systems (269921)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)785907 /
                      G:(EU-Grant)720270 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)269921},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30146661},
      UT           = {WOS:000447740300003},
      doi          = {10.1007/s10827-018-0693-9},
      url          = {https://juser.fz-juelich.de/record/851430},
}