From Heterogeneity to Function: Enhanced Modular Dynamic Range in Hierarchical Brain Networks

Abstract

Hierarchical and modular organization defines brain architecture, yet how it supports flexible computation and functional specialization remains unclear. Although dynamic range is a hallmark of efficient information processing, its distribution across modules and hierarchical levels remains largely unexplored. Here, we show that hierarchical-modular networks naturally generate heterogeneous modular dynamics and firing rates, extending regimes of critical-like dynamics with enhanced intrinsic timescales, reflecting critical slowing down, and diverse scaling exponents without fine-tuning near criticality. Intrinsic timescales increase across hierarchical levels, while their dispersion within levels emerges only when connectivity profiles and firing rates are heterogeneous. We introduce modular dynamic range to quantify how individual modules respond to external inputs and show that heterogeneity enhances both global and module-specific sensitivity, with modular intrinsic timescales increasing with dynamic range. Local stimulation reduces dynamic range in homogeneous networks but enhances it in heterogeneous networks relative to global stimulation. Moreover, selectively stimulating intermediate hierarchical levels maximizes both global and modular dynamic range, revealing a principle of functional specialization. In heterogeneous networks, modular dynamic range is more variable and increases even in unstimulated modules, demonstrating distributed computational benefits beyond directly stimulated regions. Together, these findings establish heterogeneity as a mechanism through which hierarchical-modular networks sustain critical-like dynamics, generate hierarchical timescales, and distribute computational sensitivity, providing a basis for functional specialization without fine-tuning to criticality.

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BibTeX RIS

BibTeX

@misc{torres2026from,
  title = {{From Heterogeneity to Function: Enhanced Modular Dynamic Range in Hierarchical Brain Networks}},
  author = {Filipe V. Torres and Mauro Copelli and Leonardo L. Gollo},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.32328},
  eprint = {2610.32328},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.32328},
}

RIS

TY  - UNPB
TI  - From Heterogeneity to Function: Enhanced Modular Dynamic Range in Hierarchical Brain Networks
AU  - Filipe V. Torres
AU  - Mauro Copelli
AU  - Leonardo L. Gollo
PY  - 2026
DA  - 2026/10/23
PB  - arXiv
DO  - 10.48550/arxiv.2610.32328
UR  - https://arxiv.org/abs/2610.32328
AB  - Hierarchical and modular organization defines brain architecture, yet how it supports flexible computation and functional specialization remains unclear. Although dynamic range is a hallmark of efficient information processing, its distribution across modules and hierarchical levels remains largely unexplored. Here, we show that hierarchical-modular networks naturally generate heterogeneous modular dynamics and firing rates, extending regimes of critical-like dynamics with enhanced intrinsic timescales, reflecting critical slowing down, and diverse scaling exponents without fine-tuning near criticality. Intrinsic timescales increase across hierarchical levels, while their dispersion within levels emerges only when connectivity profiles and firing rates are heterogeneous. We introduce modular dynamic range to quantify how individual modules respond to external inputs and show that heterogeneity enhances both global and module-specific sensitivity, with modular intrinsic timescales increasing with dynamic range. Local stimulation reduces dynamic range in homogeneous networks but enhances it in heterogeneous networks relative to global stimulation. Moreover, selectively stimulating intermediate hierarchical levels maximizes both global and modular dynamic range, revealing a principle of functional specialization. In heterogeneous networks, modular dynamic range is more variable and increases even in unstimulated modules, demonstrating distributed computational benefits beyond directly stimulated regions. Together, these findings establish heterogeneity as a mechanism through which hierarchical-modular networks sustain critical-like dynamics, generate hierarchical timescales, and distribute computational sensitivity, providing a basis for functional specialization without fine-tuning to criticality.
ER  -

Abstract

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