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

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.

arXiv comment23 pages, 5 figures

References in corpus (24)

• Synchronization in complex networks
• Emergent complex neural dynamics
• Community structure in directed networks
• Modularity from Fluctuations in Random Graphs and Complex Networks
• Untangling cross-frequency coupling in neuroscience
• Colloquium: Criticality and dynamical scaling in living systems
• Criticality in the brain: A synthesis of neurobiology, models and cognition
• The physics of brain network structure, function, and control
• Macroscopic description for networks of spiking neurons
• Entangled networks, synchronization, and optimal network topology
• Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review
• Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
• Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs
• Frustrated hierarchical synchronization and emergent complexity in the human connectome network
• Dynamics of noisy oscillator populations beyond the Ott-Antonsen ansatz
• Control of criticality and computation in spiking neuromorphic networks with plasticity
• Emergence of assortative mixing between clusters of cultured neurons
• Edge of chaos and avalanches in neural networks with heavy-tailed synaptic weight distribution
• Noise-Induced Synchronization, Desynchronization, and Clustering in Globally Coupled Nonidentical Oscillators
• Hybrid-type synchronization transitions: where marginal coherence, scale-free avalanches, and bistability live together
• Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity
• PDMS microfluidic film for in vitro engineering of mesoscale neuronal networks
• Sampling effects and measurement overlap can bias the inference of neuronal avalanches
• Collective excitability in a mesoscopic neuronal model of epileptic activity