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@ARTICLE{Duarte:862792,
      author       = {Duarte, Renato and Morrison, Abigail},
      title        = {{L}everaging heterogeneity for neural computation with
                      fading memory in layer 2/3 cortical microcircuits},
      journal      = {PLoS Computational Biology},
      volume       = {15},
      number       = {4},
      issn         = {1553-7358},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2019-03013},
      pages        = {e1006781 -},
      year         = {2019},
      abstract     = {Complexity and heterogeneity are intrinsic to
                      neurobiological systems, manifest in every process, at every
                      scale, and are inextricably linked to the systems’
                      emergent collective behaviours and function. However, the
                      majority of studies addressing the dynamics and
                      computational properties of biologically inspired cortical
                      microcircuits tend to assume (often for the sake of
                      analytical tractability) a great degree of homogeneity in
                      both neuronal and synaptic/connectivity parameters. While
                      simplification and reductionism are necessary to understand
                      the brain’s functional principles, disregarding the
                      existence of the multiple heterogeneities in the cortical
                      composition, which may be at the core of its computational
                      proficiency, will inevitably fail to account for important
                      phenomena and limit the scope and generalizability of
                      cortical models. We address these issues by studying the
                      individual and composite functional roles of heterogeneities
                      in neuronal, synaptic and structural properties in a
                      biophysically plausible layer 2/3 microcircuit model, built
                      and constrained by multiple sources of empirical data. This
                      approach was made possible by the emergence of large-scale,
                      well curated databases, as well as the substantial
                      improvements in experimental methodologies achieved over the
                      last few years. Our results show that variability in single
                      neuron parameters is the dominant source of functional
                      specialization, leading to highly proficient microcircuits
                      with much higher computational power than their homogeneous
                      counterparts. We further show that fully heterogeneous
                      circuits, which are closest to the biophysical reality, owe
                      their response properties to the differential contribution
                      of different sources of heterogeneity.},
      cin          = {INM-6 / IAS-6 / INM-10 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / $I:(DE-82)080012_20140620$},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      W2Morrison - W2/W3 Professorinnen Programm der
                      Helmholtzgemeinschaft (B1175.01.12) / SMHB - Supercomputing
                      and Modelling for the Human Brain (HGF-SMHB-2013-2017) /
                      Functional Neural Architectures $(jinm60_20190501)$},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)B1175.01.12 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      $G:(DE-Juel1)jinm60_20190501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31022182},
      UT           = {WOS:000467530600025},
      doi          = {10.1371/journal.pcbi.1006781},
      url          = {https://juser.fz-juelich.de/record/862792},
}