Cell-type-specific modularity in E/I networks shapes dynamical regimes and computational capabilities
Abstract
Structural modularity is a ubiquitous feature of brain networks that shapes neural dynamics and information processing. Yet excitatory and inhibitory populations can differ in their modular organization, and it remains unclear how this asymmetry affects network dynamics and computation. Here, using dynamical mean-field theory and simulations of networks performing complex temporal tasks, we show that independently tuning excitatory and inhibitory modularity controls the dynamical regimes of balanced E/I networks. In particular, such tuning can generate a dominant spectral outlier despite overall E/I balance, producing a topology-controlled transition between bulk- and low-rank-dominated regimes, with different dynamical bifurcations leading to chaos and bistability, respectively. In chaotic time-series prediction tasks, computational performance is enhanced near these dynamical boundaries, including both the onset of chaos and the transition to bistability. Favorable configurations exhibit strongly modular excitation and comparatively homogeneous inhibition, similar to what is observed in cortical connectivity.
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BibTeX
@misc{giannakakis2026celltypespecific,
title = {{Cell-type-specific modularity in E/I networks shapes dynamical regimes and computational capabilities}},
author = {Emmanouil Giannakakis and Victor Buendía and Oleg Vinogradov and Anna Levina},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.06720},
eprint = {2611.06720},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.06720},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Cell-type-specific modularity in E/I networks shapes dynamical regimes and computational capabilities
AU - Emmanouil Giannakakis
AU - Victor Buendía
AU - Oleg Vinogradov
AU - Anna Levina
PY - 2026
DA - 2026/11/04
PB - arXiv
DO - 10.48550/arxiv.2611.06720
UR - https://arxiv.org/abs/2611.06720
AB - Structural modularity is a ubiquitous feature of brain networks that shapes neural dynamics and information processing. Yet excitatory and inhibitory populations can differ in their modular organization, and it remains unclear how this asymmetry affects network dynamics and computation. Here, using dynamical mean-field theory and simulations of networks performing complex temporal tasks, we show that independently tuning excitatory and inhibitory modularity controls the dynamical regimes of balanced E/I networks. In particular, such tuning can generate a dominant spectral outlier despite overall E/I balance, producing a topology-controlled transition between bulk- and low-rank-dominated regimes, with different dynamical bifurcations leading to chaos and bistability, respectively. In chaotic time-series prediction tasks, computational performance is enhanced near these dynamical boundaries, including both the onset of chaos and the transition to bistability. Favorable configurations exhibit strongly modular excitation and comparatively homogeneous inhibition, similar to what is observed in cortical connectivity.
ER -