Modular architecture facilitates noise-driven control of synchrony in neuronal networks
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- Hideaki Yamamoto
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan.
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- F. Paul Spitzner
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
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- Taiki Takemuro
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan.
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- Victor Buendía
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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- Hakuba Murota
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan.
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- Carla Morante
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain.
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- Tomohiro Konno
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan.
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- Shigeo Sato
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan.
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- Ayumi Hirano-Iwata
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan.
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- Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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- Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
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- Miguel A. Muñoz
- Departamento de Electromagnetismo y Física de la Materia, Universidad de Granada, Granada, Spain.
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- Johannes Zierenberg
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
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- Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain.
説明
<jats:p>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.</jats:p>
収録 刊行 物
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- Science Advances
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Science Advances 9 (34), 2023-08-25
American Association for the Advancement of Science (AAAS)