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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},
}