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

TL;DR

This study shows that modular brain network architecture enables external noise to control synchronization states, using neuroengineering experiments, computational modeling, and a mesoscopic framework to understand the underlying mechanisms.

Contribution

It introduces a novel insight that modular architecture allows noise to modulate synchrony, combining experimental, computational, and theoretical approaches.

Findings

Modular networks desynchronize more easily under asynchronous stimulation.

External noise reduces synaptic resources, promoting stochastic intermodular interactions.

A mesoscopic model with state-dependent gating explains noise-induced desynchronization.

Abstract

Brain functions require both segregated processing of information in specialized circuits, as well as integration across circuits to perform high-level information processing. One possible way to implement these seemingly opposing demands is by flexibly switching between synchronous and less synchronous states. Understanding how complex synchronization patterns are controlled by the interaction of network architecture and external perturbations is thus a central challenge in neuroscience, but the mechanisms behind such interactions remain elusive. Here, we utilise precision neuroengineering to manipulate cultured neuronal networks and show that a modular architecture facilitates desynchronization upon asynchronous stimulation, making external noise a control parameter of synchrony. Using spiking neuron models, we then demonstrate that external noise can reduce the level of available synaptic resources, which make intermodular interactions more stochastic and thereby facilitates the breakdown of synchrony. Finally, the phenomenology of stochastic intermodular interactions is formulated into a mesoscopic model that incorporates a state-dependent gating mechanism for signal propagation. Taken together, our results demonstrate a network mechanism by which asynchronous inputs tune the inherent dynamical state in structured networks of excitable units.