2026-04-13T00:52:43Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2214902025-12-04T20:44:36Zcom_2072_1057col_2072_478822col_2072_478914col_2072_478917

00925njm 22002777a 4500 dc Yamamoto, Hideaki author Spitzner, F. Paul author Takemuro, Taiki author Buendía, Victor author Murota, Hakuba author Morante, Carla author Konno, Tomohiro author Sato, Shigeo author Hirano-Iwata, Ayumi author Levina, Anna author Priesemann, Viola author Muñoz Pérez, Miguel Ángel author Zierenberg, Johannes author Soriano i Fradera, Jordi author 2025-06-11T15:45:33Z 2025-06-11T15:45:33Z 2023-08-25 2025-06-11T15:45:33Z High-level information processing in the mammalian cortex requires both egregated 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. Xarxes neuronals (Informàtica) Mamífers Mètodes de simulació Neural networks (Computer science) Mammals Simulation methods Modular architecture facilitates noise-driven control of synchrony in neuronal networks