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@INPROCEEDINGS{MoralesGregorio:878134,
      author       = {Morales-Gregorio, Aitor and Dabrowska, Paulina and Gutzen,
                      Robin and Yegenoglu, Alper and Diaz, Sandra and Palmis,
                      Sarah and Paneri, Sofia and Rene, Alexandre and Sapountzis,
                      Panagiotis and Diesmann, Markus and Grün, Sonja and Senk,
                      Johanna and Gregoriou, Georgia and Kilavik, Bjorg and van
                      Albada, Sacha},
      title        = {{E}stimation of the cortical microconnectome from in vivo
                      spiking activity in the macaque monkey},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2020-02652},
      year         = {2020},
      abstract     = {The typical range of local connectivity in the cerebral
                      cortex delineates columnar microcircuits, within which the
                      layer- and population-specific connectivities present
                      features that are preserved across species and cortical
                      areas. However, when considered in more detail, the internal
                      connectivity structure, i.e. the microconnectome (MC), of
                      such microcircuits is variable across cortical areas.
                      Furthermore, the parameters describing the MC are largely
                      unknown for most cortical areas.Models constructed based on
                      structural data have been able to recover realistic
                      first-order spike train statistics in early sensory cortical
                      areas [1, 2]. These bottom-up models can be constructed
                      owing to the availability of extensive anatomical and
                      physiological data from early visual and somatosensory
                      areas. However, such measurements are less abundant for
                      higher-order cortices, limiting bottom-up modeling until
                      further biological measurements are published.Here we
                      present an analysis that aims to overcome some of the
                      limitations in currently available anatomical data. We use
                      experimentally measured electrophysiological activity from
                      vision-related and motor areas to constrain the connectivity
                      of cortical microcircuit models and infer area-specific
                      features of the MC. The novel experimental data consist of
                      simultaneous layer-resolved laminar recordings from macaque
                      primary motor (M1) and premotor (PMd) cortices [3]; as well
                      as acute simultaneous recordings of macaque dorsolateral
                      prefrontal cortex (dlPFC) and visual area V4. All data were
                      recorded during resting-state sessions, i.e. while the
                      subjects were not performing any task. Data from the resting
                      state are expected to deliver rich dynamics related to the
                      underlying connectivity structure [4].We explore the
                      parameter space of the MC with an evolutionary algorithm
                      using biologically inspired spiking cortical microcircuit
                      models. During the parameter estimation phase, a set of
                      standardized statistical tests, based on established
                      single-neuron and population statistics [5], are used to
                      score the similarity between the simulated data and
                      experimental recordings. The score is calculated based on
                      the overlap between experimental and simulated data
                      statistics via the Wasserstein distance. Parameter estimates
                      are obtained by maximizing this score, and are then
                      validated against a separate set of statistics, which were
                      not used in the estimation phase. Finally, we assess the
                      similarities and differences of estimated model parameters
                      across areas.Future work will integrate these local visual
                      and motor models into a large-scale visuomotor cortical
                      multi-area model, extending the work in [2, 6].References:1.
                      Potjans TC, Diesmann M. Cereb Cortex 2014, 24(3),
                      785–8062. Schmidt M, Bakker R et al. Brain Struct Func
                      2017, 223, 1409–14353. Kilavik BE. SfN 2018. Online4.
                      Dąbrowska P, Voges N et al. On the complexity of resting
                      state spiking activity in monkey motor cortex. In
                      preparation5. Gutzen R, von Papen M et al. Front Neuroinform
                      2018, 12:906. Schmidt M, Bakker R et al. PLOS CB 2018, 14,
                      e1006359},
      month         = {Jul},
      date          = {2020-07-18},
      organization  = {29th Annual Computational Neuroscience
                       Meeting CNS*2020, Online (Online), 18
                       Jul 2020 - 23 Jul 2020},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
                      modelling and simulation (POF3-574) / SPP 2041 347572269 -
                      Integration von Multiskalen-Konnektivität und
                      Gehirnarchitektur in einem supercomputergestützten Modell
                      der menschlichen Großhirnrinde (347572269) / GRK 2416 - GRK
                      2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / FLAG-ERA III - The Flagship ERA-NET
                      — FLAG-ERA III (825207)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(GEPRIS)347572269 / G:(GEPRIS)368482240 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(EU-Grant)825207},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/878134},
}