CNS*2026 Halifax

Adrián Ponce-Alvarez^*1,,2,3 and Germán Sumbre^4
1 Departament de Matemàtiques, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain.
² Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech), Barcelona, Spain.
³ Centre de Recerca Matemàtica, Barcelona, Spain.
⁴ Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France


*Email : [email protected]

Introduction
Neuronal activity shows statistics consistent with a critical point, a regime that maximize information capacity. Yet, the role of different cell types remains largely unexplored. Models [1] and in vitro studies [2] suggest that excitation–inhibition (E/I) balance is key for self-organized criticality, but how E and I dynamics interact during in vivo critical activity is unclear. Similarly, glial cells such as radial astrocytes (RAs) regulate neuronal function [3], but their role in criticality is unknown. Here, we studied how E/I neuronal activity and astrocyte calcium dynamics contribute to criticality by combining transgenic zebrafish with cell-type-specific calcium indicators, a stochastic network, and model inference.

Methods
Spontaneous neuronal activity in the optic tectum (OT) of 10 zebrafish larvae was recorded using light-sheet microscopy. A double-transgenic line expressing GCaMP6f in all neurons and Vglut in glutamatergic neurons identified of E and I cells. Two-photon calcium imaging was performed in 7 larvae expressing GCaMP6f in neurons and RCaMP1b in RAs [3]. OT activity was recorded during spontaneous activity and after mild electrical stimulation, which triggered synchronized Ca²⁺ transients in RAs.
E and I activity was modelled using a stochastic network displaying critical avalanches at a E/I phase transition [1]. The maximum entropy principle mapped neuronal activity onto statistical models [4], quantifying criticality and detecting deviations.

Results
Our results show that neuronal avalanches approached criticality when E and I activity were balanced. Notably, the model accurately captured the observed avalanche statistics and their sensitivity to E/I fluctuations around a critical point defined by balanced excitatory and inhibitory synaptic strengths, where balanced amplification drives network avalanches. Furthermore, we found that RA synchronization shifted tectal neuronal activity away from its spontaneous critical state toward a more ordered regime, with a reduced repertoire of network states and diminished susceptibility to external inputs. These findings demonstrate that glial activity can actively regulate the state of neuronal ensembles, including their proximity to criticality.

Discussion
Extensive research highlights the benefits of E/I balance and critical dynamics. Balanced networks enhance amplification, selectivity, and stability, while critical dynamics optimize information processing. Here, we show that neuronal avalanche statistics and their dependence on spontaneous E/I fluctuations in the zebrafish OT match a model reaching criticality at balanced E and I couplings. Moreover, RA synchronization in the OT reshapes collective neuronal activity, consistent with a shift from spontaneous critical dynamics to a more ordered subcritical regime. Our findings show that radial astrocyte activity can shift the state of neuronal ensembles and modulate their proximity to criticality.

References
1.     Benayoun, M., et al. (2010). Avalanches in a Stochastic Model of Spiking Neurons. PLoS Comput Biol, 6(7), e1000846. https://doi.org/10.1371/journal.pcbi.1000846
2.     Shew, W.L., et al. (2011). Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J Neurosci., 31(1), 55-63. https://doi.org/10.1523/JNEUROSCI.4637-10.2011
3.     Uribe-Arias, A., et al. (2023). Radial astrocyte synchronization modulates the visual system during behavioral-state transitions. Neuron 111, (24), 4040-4057.e6. https://doi.org/10.1016/j.neuron.2023.09.022
4.     Tkačik, G., et al. (2014). Searching for Collective Behavior in a Large Network of Sensory Neurons. PLoS Comput Biol, 10(1), e1003408. https://doi.org/10.1371/journal.pcbi.1003408

Acknowledgments
This study was supported by the Project PID2022-137708NB-I00 funded by MICIU/AEI /10.13039/501100011033 and FEDER, UE. A. Ponce-Alvarez was supported by a Ramón y Cajal fellowship (RYC2020-029117-I) funded by MICIU/AEI/10.13039/501100011033 and “ESF Investing in your future”. G. Sumbre was supported by ERC CoG 726280.