Modular architecture facilitates noise-driven control of synchrony in neuronal 
networks

Yamamoto, H; Spitzner, FP; Takemuro, T; Buendía, V; Murota, H; Morante, C; Konno, T; Sato, S; Hirano-Iwata, A; Levina, A; Priesemann, V; Muñoz, MA; Zierenberg, J; Soriano, J

doi:10.1126/sciadv.ade1755

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Modular architecture facilitates noise-driven control of synchrony in neuronal networks

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Buendía, V
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Levina, A
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.science.org/doi/epdf/10.1126/sciadv.ade1755
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Citation

Yamamoto, H., Spitzner, F., Takemuro, T., Buendía, V., Murota, H., Morante, C., et al. (2023). Modular architecture facilitates noise-driven control of synchrony in neuronal networks. Science Advances, 9(34): eade1755. doi:10.1126/sciadv.ade1755.

Cite as: https://hdl.handle.net/21.11116/0000-000A-83F9-9

Abstract

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.