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@INPROCEEDINGS{vanderVlag:1046968,
      author       = {van der Vlag, Michiel and Yegenoglu, Alper and
                      Jimenez-Romero, Cristian and Morrison, Abigail and Diaz,
                      Sandra},
      title        = {{C}omplexity and {C}riticality in {N}euro-{I}nspired
                      {R}eservoirs; 1st ed. 2026},
      journal      = {Artificial Neural Networks and Machine Learning – ICANN
                      2025 / Senn, Walter (Editor)
                      [https://orcid.org/0000-0003-3622-0497] ; Cham : Springer
                      Nature Switzerland, 2026, Chapter 52 ; ISSN:
                      0302-9743=1611-3349 ; ISBN:
                      978-3-032-04557-7=978-3-032-04558-4 ;
                      doi:10.1007/978-3-032-04558-4},
      volume       = {16068},
      number       = {LNCS},
      address      = {Cham},
      publisher    = {Springer Nature Switzerland},
      reportid     = {FZJ-2025-04036},
      isbn         = {978-3-032-04557-7},
      series       = {Lecture Notes in Computer Science},
      pages        = {645 - 657},
      year         = {2026},
      note         = {This preprint has not undergone peer review (when
                      applicable) or any post-submission improvements or
                      corrections. The Version of Record of this contribution is
                      published in "Artificial Neural Networks and Machine
                      Learning – ICANN 2025", and is available online at
                      https://link.springer.com/book/10.1007/978-3-032-04558-4},
      comment      = {[Ebook] Artificial Neural Networks and Machine Learning –
                      ICANN 2025 : 34th International Conference on Artificial
                      Neural Networks, Kaunas, Lithuania, September 9–12, 2025,
                      Proceedings, Part I / Senn, Walter ; Sanguineti, Marcello ;
                      Saudargiene, Ausra ; Tetko, Igor ; Villa, Alessandro E. P. ;
                      Jirsa, Viktor K. ; Bengio, Yoshua 1st ed. 2026, Cham :
                      Springer Nature Switzerland, 2026,},
      booktitle     = {[Ebook] Artificial Neural Networks and
                       Machine Learning – ICANN 2025 : 34th
                       International Conference on Artificial
                       Neural Networks, Kaunas, Lithuania,
                       September 9–12, 2025, Proceedings,
                       Part I / Senn, Walter ; Sanguineti,
                       Marcello ; Saudargiene, Ausra ; Tetko,
                       Igor ; Villa, Alessandro E. P. ; Jirsa,
                       Viktor K. ; Bengio, Yoshua 1st ed.
                       2026, Cham : Springer Nature
                       Switzerland, 2026,},
      abstract     = {Understanding information propagation and computational
                      capabilities in large-scale brain models is crucial for
                      advancing both neuroscience and neuro-inspired computing. It
                      remains unknown how complexity affects the performance of
                      simulated biological networks in predicting non-linear
                      dynamics. Here, this issue is addressed by integrating
                      reservoir computing, a recurrent neural network
                      architecture, with The Virtual Brain, a whole-brain
                      simulation platform, to create amore
                      neurophysiologically-plausible machine learning framework.
                      Metrics derived from nonlinear dynamics and complexity
                      theory, including the largest Lyapunov exponent, which
                      captures chaotic behavior, Detrended Fluctuation Analysis,
                      which assesses temporal correlations, and the Perturbational
                      Complexity Index, which evaluates the complexity of neural
                      responses, provide a quantitative framework for
                      characterizing cognitive dynamics. Deploying this framework
                      on High Performance Computing enables a thorough exploration
                      of the vast parameter space, utilizing a diverse evaluation
                      framework that assesses simulations through these metrics,
                      implemented on Graphics Processing Units (GPUs). This
                      enables the identification of optimal parameter regimes, a
                      comprehensive characterization of the complex dynamics
                      exhibited by the system, and a deeper understanding of the
                      underlying mechanisms governing the TVB-based reservoir’s
                      computational capabilities. Ridge regression, accelerated
                      also by GPUs, is used to extract the predictive capacity
                      from the reservoir states. The results suggest that
                      edge-of-chaosdynamics correspond to enhanced memory and
                      prediction accuracy, supporting the potential of TVB-based
                      reservoirs for brain-inspired machine learning.},
      month         = {Sep},
      date          = {2025-09-09},
      organization  = {34th International Conference on
                       Artificial Neural Networks, Kaunas
                       (Lithuania), 9 Sep 2025 - 12 Sep 2025},
      cin          = {JSC},
      ddc          = {006.3},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / eBRAIN-Health -
                      eBRAIN-Health - Actionable Multilevel Health Data
                      (101058516) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)101058516 /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16 / PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-032-04558-4_52},
      url          = {https://juser.fz-juelich.de/record/1046968},
}