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@INPROCEEDINGS{Schmidt:173332,
      author       = {Schmidt, Maximilian and van Albada, Sacha and Bakker,
                      Rembrandt and Diesmann, Markus},
      title        = {{A} spiking multi-area network model of macaque visual
                      cortex},
      reportid     = {FZJ-2014-06742},
      year         = {2014},
      abstract     = {The primate visual cortex consists of a set of specialized
                      areas whose inter-connections have been shown to influence
                      its dynamics both in spontaneous and driven conditions.
                      Hitherto, models of this system have either concentrated on
                      local detailed circuits or studied the interplay of areas,
                      each represented by a few dynamical equations. We present a
                      model which bridges this gap between microscopic and
                      macroscopic dynamics by extending a spiking model of a 1mm2
                      patch of early sensory cortex [1] to all vision-related
                      areas of the macaque cortex. The single-cell dynamics is
                      kept simple in order to bring out the influence of the
                      complex connectivity, which is based on a systematic
                      synthesis of anatomical and electrophysiological findings.
                      The extension to multiple areas allows us to replace random
                      inputs to the network in part by simulated synapses, thereby
                      increasing the self-consistency of the model. Here the
                      immediate aim is not to address network function from a
                      top-down perspective but to explore the relationship between
                      network structure and fundamental multi-scale activity
                      states.Neuron densities and laminar thicknesses are taken
                      from available data sets or determined based on structural
                      regularities across the cortex. The cortico-cortical
                      connectivity is defined combining binary information from a
                      large number of data sets collected in the CoCoMac database
                      [2] with quantitative data from retrograde tracing studies
                      [3], which is completed by exploiting an exponential decay
                      of connection densities over distance [4]. Furthermore, we
                      implement laminar connection patterns [5] and estimate
                      missing data using a sigmoidal relation between the fraction
                      of supragranularly originating projections and architectural
                      types of areas [6]. We perform simulations of the system
                      using NEST and find a broad parameter regime with
                      asynchronous, irregular spiking across populations,
                      characteristic of spontaneous cortical activity. The rich
                      connectivity structure is reflected in a complex pattern of
                      firing rates across areas and populations, where inhibitory
                      neurons show higher activity than excitatory cells despite
                      identical intrinsic dynamics.},
      month         = {Nov},
      date          = {2014-11-15},
      organization  = {Annual meeting of the SfN, Washington,
                       DC (USA), 15 Nov 2014 - 19 Nov 2014},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / 89574 - Theory, modelling and simulation
                      (POF2-89574) / BRAINSCALES - Brain-inspired multiscale
                      computation in neuromorphic hybrid systems (269921) /
                      Brain-Scale Simulations $(jinb33_20121101)$ / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP - The Human Brain Project
                      (604102) / BTN-Peta - The Next-Generation Integrated
                      Simulation of Living Matter (BTN-Peta-2008-2012) /
                      Brain-Scale Simulations $(jinb33_20121101)$},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-HGF)POF2-89574 /
                      G:(EU-Grant)269921 / $G:(DE-Juel1)jinb33_20121101$ /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)604102 /
                      G:(DE-Juel1)BTN-Peta-2008-2012 /
                      $G:(DE-Juel1)jinb33_20121101$},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/173332},
}