% 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{Gutzen:916176,
      author       = {Gutzen, Robin and Grün, Sonja and Denker, Michael},
      title        = {{E}valuating the statistical similarity of neural network
                      activity and connectivity via eigenvector angles},
      journal      = {Biosystems},
      volume       = {223},
      issn         = {0011-4014},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-05996},
      pages        = {104813},
      year         = {2023},
      abstract     = {Neural systems are networks, and strategic comparisons
                      between multiple networks are a prevalent task in many
                      research scenarios. In this study, we construct a
                      statistical test for the comparison of matrices representing
                      pairwise aspects of neural networks, in particular, the
                      correlation between spiking activity and connectivity. The
                      ”eigenangle test” quantifies the similarity of two
                      matrices by the angles between their ranked eigenvectors. We
                      calibrate the behavior of the test for use with correlation
                      matrices using stochastic models of correlated spiking
                      activity and demonstrate how it compares to classical
                      two-sample tests, such as the Kolmogorov–Smirnov distance,
                      in the sense that it is able to evaluate also structural
                      aspects of pairwise measures. Furthermore, the principle of
                      the eigenangle test can be applied to compare the similarity
                      of adjacency matrices of certain types of networks. Thus,
                      the approach can be used to quantitatively explore the
                      relationship between connectivity and activity with the same
                      metric. By applying the eigenangle test to the comparison of
                      connectivity matrices and correlation matrices of a random
                      balanced network model before and after a specific synaptic
                      rewiring intervention, we gauge the influence of
                      connectivity features on the correlated activity. Potential
                      applications of the eigenangle test include simulation
                      experiments, model validation, and data analysis.},
      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          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / HAF -
                      Helmholtz Analytics Framework (ZT-I-0003) / JL SMHB - Joint
                      Lab Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / Open-Access-Publikationskosten
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)ZT-I-0003 / G:(DE-Juel1)JL
                      SMHB-2021-2027 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36460172},
      UT           = {WOS:000899830200001},
      doi          = {10.1016/j.biosystems.2022.104813},
      url          = {https://juser.fz-juelich.de/record/916176},
}