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@INPROCEEDINGS{Kurth:874323,
      author       = {Kurth, Anno and Morales-Gregorio, Aitor and van Meegen,
                      Alexander and Pronold, Jari and Korcsak-Gorzo, Agnes and
                      Vollenbröker, Hannah and Bakker, Rembrandt and Diesmann,
                      Markus and van Albada, Sacha},
      title        = {{M}ulti-area model of macaque cortex as a scaffold model
                      and workflow testcase},
      reportid     = {FZJ-2020-01371},
      year         = {2020},
      abstract     = {Multi-area model of macaque cortex as a scaffold model and
                      workflow test caseAnno Kurth, Alexander van Meegen, Aitor
                      Morales-Gregorio, Jari Pronold, Agnes Korcsak-Gorzo, Hannah
                      Vollenbröker, Rembrandt Bakker, Markus Diesmann and Sacha J
                      van AlbadaThere are many open questions on the relationships
                      between the structure, dynamics and function of the brain,
                      especially from a multi-modal perspective bridging micro-,
                      meso- and macroscopic scales. Large-scale point neuron
                      network models of cortical areas and their interconnections,
                      integrating vast bodies of anatomical data, provide
                      researchers with tools to investigate these issues. In order
                      to make reliable steps in understanding, we need to take an
                      incremental approach to the design of the models, and ensure
                      that they can be built on by others.Here we present a
                      multi-area model (MAM) describing all 32 areas of the
                      macaque vision-related cortex [1] that serves as a scaffold
                      for relating brain structure to its dynamics and function on
                      multiple scales. The model connectivity is determined by
                      processing available anatomical data into a layer-resolved
                      connectome [2] of macaque vision-related cortex. A spiking
                      neural network with the specified connectivity is
                      constructed using NEST [3] and simulated on a supercomputer
                      to study resting-state activity.The model is being extended
                      and refined in various directions: In one project, the
                      motor-related cortical areas are being added, thereby
                      enabling the study of visuo-motor integration in a unified,
                      biologically realistic framework. Mechanisms of spatial
                      attention are being implemented as a first step towards
                      modeling visual processing. Moreover, ongoing work explores
                      the possibility of endowing the anatomically based model
                      with information processing capabilities through learning
                      methods for spiking neural networks [4]. The methods devised
                      to create the macaque model are further generalized to
                      construct a model of the human visual cortex taking into
                      account different neuron characteristics [5] and different
                      anatomical constraints obtained via diffusion imaging
                      [6].Finally, a fully digitized illustrative workflow is
                      provided alongside the MAM to ensure reproducibility and
                      enable re-use by the community. All code is available on
                      GitHub. The tool Snakemake [7] provides a reproducible and
                      user-friendly framework for the execution of the model. The
                      workflow from the anatomical data to the simulation code,
                      analysis and visualization can serve as an example for
                      similar data-driven brain models.[1] Schmidt, M., Bakker,
                      R., Shen, K., Bezgin, G., Diesmann, M., $\&$ van Albada, S.
                      J. (2018). A multi-scale layer-resolved spiking network
                      model of resting-state dynamics in macaque visual cortical
                      areas. PLOS Computational Biology, 14(10).[2] Schmidt, M.,
                      Bakker, R., Hilgetag, C. C., Diesmann, M., $\&$ van Albada,
                      S. J. (2018). Multi-scale account of the network structure
                      of macaque visual cortex. Brain Structure and Function,
                      223(3), 1409-1435.[3] Peyser, A. et al. (2017). NEST 2.14.0.
                      Zenodo. 10.5281/zenodo.882971. [4] Bellec, G., Salaj, D.,
                      Subramoney, A., Legenstein, R., Maass, W. (2018). Long
                      short-term memory and learning-to-learn in networks of
                      spiking neurons. Advances in Neural Information Processing
                      Systems, 787-797.[5] Teeter, C., et al. (2018). Generalized
                      leaky integrate-and-fire models classify multiple neuron
                      types. Nature Communications, 9, 709.[6] Van Essen, D. C.,
                      et al. (2013). The WU-Minn human connectome project: an
                      overview. Neuroimage, 80, 62-79.[7] Köster, J., $\&$
                      Rahmann, S. (2012). Snakemake—a scalable bioinformatics
                      workflow engine. Bioinformatics, 28(19), 2520-2522.},
      month         = {Feb},
      date          = {2020-02-03},
      organization  = {Human Brain Project Summit 2020,
                       Athens (Greece), 3 Feb 2020 - 6 Feb
                       2020},
      subtyp        = {Other},
      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          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / JL SMHB - Joint Lab Supercomputing and Modeling
                      for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)785907 / G:(DE-Juel1)JL
                      SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/874323},
}