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001046968 1001_ $$0P:(DE-Juel1)179447$$avan der Vlag, Michiel$$b0$$eCorresponding author
001046968 1112_ $$a34th International Conference on Artificial Neural Networks$$cKaunas$$d2025-09-09 - 2025-09-12$$gICANN$$wLithuania
001046968 245__ $$aComplexity and Criticality in Neuro-Inspired Reservoirs
001046968 250__ $$a1st ed. 2026
001046968 260__ $$aCham$$bSpringer Nature Switzerland$$c2026
001046968 29510 $$a[Ebook] Artificial Neural Networks and Machine Learning – ICANN 2025 : 34th International Conference on Artificial Neural Networks, Kaunas, Lithuania, September 9–12, 2025, Proceedings, Part I / Senn, Walter ; Sanguineti, Marcello ; Saudargiene, Ausra ; Tetko, Igor ; Villa, Alessandro E. P. ; Jirsa, Viktor K. ; Bengio, Yoshua 1st ed. 2026, Cham : Springer Nature Switzerland, 2026,
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001046968 4900_ $$aLecture Notes in Computer Science$$v16068
001046968 500__ $$aThis 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
001046968 520__ $$aUnderstanding information propagation and computational capabilities in large-scale brain models is crucial for advancing both neuroscience and neuro-inspired computing. It remains unknown how complexity affects the performance of simulated biological networks in predicting non-linear dynamics. Here, this issue is addressed by integrating reservoir computing, a recurrent neural network architecture, with The Virtual Brain, a whole-brain simulation platform, to create amore neurophysiologically-plausible machine learning framework. Metrics derived from nonlinear dynamics and complexity theory, including the largest Lyapunov exponent, which captures chaotic behavior, Detrended Fluctuation Analysis, which assesses temporal correlations, and the Perturbational Complexity Index, which evaluates the complexity of neural responses, provide a quantitative framework for characterizing cognitive dynamics. Deploying this framework on High Performance Computing enables a thorough exploration of the vast parameter space, utilizing a diverse evaluation framework that assesses simulations through these metrics, implemented on Graphics Processing Units (GPUs). This enables the identification of optimal parameter regimes, a comprehensive characterization of the complex dynamics exhibited by the system, and a deeper understanding of the underlying mechanisms governing the TVB-based reservoir’s computational capabilities. Ridge regression, accelerated also by GPUs, is used to extract the predictive capacity from the reservoir states. The results suggest that edge-of-chaosdynamics correspond to enhanced memory and prediction accuracy, supporting the potential of TVB-based reservoirs for brain-inspired machine learning.
001046968 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001046968 536__ $$0G:(EU-Grant)101058516$$aeBRAIN-Health - eBRAIN-Health - Actionable Multilevel Health Data (101058516)$$c101058516$$fHORIZON-INFRA-2021-TECH-01$$x1
001046968 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x2
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001046968 650_0 $$aArtificial intelligence
001046968 650_0 $$aComputers
001046968 650_0 $$aApplication software
001046968 650_0 $$aComputer networks
001046968 7001_ $$0P:(DE-Juel1)161462$$aYegenoglu, Alper$$b1
001046968 7001_ $$0P:(DE-Juel1)184894$$aJimenez-Romero, Cristian$$b2$$ufzj
001046968 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b3
001046968 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b4
001046968 773__ $$a10.1007/978-3-032-04558-4_52$$nLNCS$$tArtificial Neural Networks and Machine Learning – ICANN 2025 / Senn, Walter (Editor) [https://orcid.org/0000-0003-3622-0497] ; Cham : Springer Nature Switzerland, 2026, Chapter 52 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-032-04557-7=978-3-032-04558-4 ; doi:10.1007/978-3-032-04558-4$$v16068$$y2026
001046968 8564_ $$uhttps://link.springer.com/book/10.1007/978-3-032-04558-4
001046968 8564_ $$uhttps://juser.fz-juelich.de/record/1046968/files/Complexity%20and%20Criticality%20in%20Neuro-Inspired%20Reservoirs.pdf$$yOpenAccess
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