% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Capone:1006405,
      author       = {Capone, Cristiano and De Luca, Chiara and De Bonis, Giulia
                      and Gutzen, Robin and Bernava, Irene and Pastorelli, Elena
                      and Simula, Francesco and Lupo, Cosimo and Tonielli,
                      Leonardo and Resta, Francesco and Allegra Mascaro, Anna
                      Letizia and Pavone, Francesco and Denker, Michael and
                      Paolucci, Pier Stanislao},
      title        = {{S}imulations approaching data: cortical slow waves in
                      inferred models of the whole hemisphere of mouse},
      journal      = {Communications biology},
      volume       = {6},
      number       = {1},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2023-01645},
      pages        = {266},
      year         = {2023},
      abstract     = {The development of novel techniques to record wide-field
                      brain activity enables estimation of data-driven models from
                      thousands of recording channels and hence across large
                      regions of cortex. These in turn improve our understanding
                      of the modulation of brain states and the richness of
                      traveling waves dynamics. Here, we infer data-driven models
                      from high-resolution in-vivo recordings of mouse brain
                      obtained from wide-field calcium imaging. We then assimilate
                      experimental and simulated data through the characterization
                      of the spatio-temporal features of cortical waves in
                      experimental recordings. Inference is built in two steps: an
                      inner loop that optimizes a mean-field model by likelihood
                      maximization, and an outer loop that optimizes a periodic
                      neuro-modulation via direct comparison of observables that
                      characterize cortical slow waves. The model reproduces most
                      of the features of the non-stationary and non-linear
                      dynamics present in the high-resolution in-vivo recordings
                      of the mouse brain. The proposed approach offers new methods
                      of characterizing and understanding cortical waves for
                      experimental and computational neuroscientists.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5235 -
                      Digitization of Neuroscience and User-Community Building
                      (POF4-523) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5235 /
                      G:(EU-Grant)945539 / G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36914748},
      UT           = {WOS:000948919700001},
      doi          = {10.1038/s42003-023-04580-0},
      url          = {https://juser.fz-juelich.de/record/1006405},
}