P099 Critical Slowing Down and the Hierarchy of Neural Timescales: A Unified Framework
Leonardo L. Gollo*1,2
1Brain Networks and Modelling Laboratory and The Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia. 2Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Palma de Mallorca, Spain
*Email: leonardo@ifisc.uib-csic.es
IntroductionResearch on brain criticality often focuses on identifying phase transitions, typically assuming that brain dynamics can be described by a single control parameter [1]. However, this approach overlooks the inherent heterogeneity of neural systems. At the neuronal level, diversity in excitability gives rise to multiple percolations and transitions, leading to complex dynamical behaviors [2]. At the macroscopic level, this heterogeneity enables the brain to operate across a broad hierarchy of timescales [3], ranging from rapid neural responses to external stimuli to slower cognitive processes [4,5]. A critical open question is how the framework of brain criticality, which emphasizes phase transitions, can be reconciled with the observed hierarchy of neural timescales. MethodsWe employed a theoretical framework integrating nonlinear dynamics and criticality theory to analyze the relationship between hierarchical timescales and proximity to criticality. Specifically, we examined the role of critical slowing down, a phenomenon in which systems near a phase transition exhibit prolonged recovery times following perturbations. Using existing empirical findings on functional brain hierarchy and criticality [6,7,8], we evaluated how regions with slower timescales align with the principles of critical slowing down. Additionally, we explored how this framework supports a balance between sensitivity and stability in neural information processing [9]. ResultsOur analysis indicates that brain regions are not uniformly critical but instead positioned at varying distances from criticality. Regions with slower timescales tend to be situated closer to the critical point due to critical slowing down, while regions with faster dynamics operate in subcritical regimes. This spatiotemporal organization supports a structured coexistence of critical and subcritical dynamics, which enhances both sensitivity to external stimuli and reliable internal processing. Furthermore, this framework naturally gives rise to a hierarchy of timescales, and the coexistence of critical and subcritical dynamics enables a balance between flexibility and robustness, allowing neural systems to dynamically regulate information flow and cognitive processes [9]. Discussion By integrating brain criticality and hierarchical timescales, our findings offer a novel perspective on neural dynamics. Instead of a uniform critical state, we propose that brain regions exist on a criticality continuum, shaped by their functional roles and temporal properties. This unified framework provides a nonlinear dynamics explanation for the brain’s timescale-based hierarchy, shedding light on its neurophysiological mechanisms. By bridging criticality and hierarchical organization, this work advances our understanding of the fundamental principles governing brain dynamics, offering a foundation for future investigations into neural computation and cognition.
Acknowledgements We thank our colleagues and collaborators for their insightful discussions and feedback, which have enriched the development of this work. This work was supported by the Australian Research Council (ARC) Future Fellowship (FT200100942), the Ramón y Cajal Fellowship (RYC2022-035106-I), and the María de Maeztu Program for units of Excellence in R&D, grant CEX2021-001164-M/10.13039/501100011033. References1.https://doi.org/10.1016/j.pneurobio.2017.07.002 2.https://doi.org/10.7717/peerj.1912 3.https://doi.org/10.1371/journal.pcbi.1000209 4.https://doi.org/10.1098/rstb.2014.0165 5.https://doi.org/10.1523/JNEUROSCI.1699-24.2024 6.https://doi.org/10.1073/pnas.2208998120 7.https://doi.org/10.1371/journal.pcbi.1010919 8.https://doi.org/10.1103/PhysRevX.14.031021 9.https://doi.org/10.1098/rsif.2017.0207
