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Related Experiment Video

Updated: Apr 10, 2026

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Synchrony in stochastically driven neuronal networks with complex topologies.

Katherine A Newhall¹, Maxim S Shkarayev², Peter R Kramer³

¹Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3250, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 13, 2015

Summary

We investigated neuronal synchronization in a scale-free network, finding that firing rate depends only on external drive. Network synchrony relies on balancing input fluctuations and synaptic coupling.

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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Area of Science:

Computational neuroscience
Network science
Complex systems

Background:

Neuronal synchronization is crucial for information processing.
Scale-free networks with high clustering are common in biological systems.
Understanding synchronization dynamics in such networks is challenging.

Purpose of the Study:

To analyze synchronization in a stochastically driven neuronal network.
To investigate the role of network structure (preferential attachment, high clustering) on synchronization.
To develop accurate analytical predictions for cascading total firing events.

Main Methods:

Utilized a current-based, integrate-and-fire neuronal model.
Employed a preferential-attachment network with scale-free properties.
Conducted a second-order calculation beyond mean-field approximations.
Performed direct numerical simulations for validation.

Main Results:

Firing rate in the synchronous state is independent of synaptic coupling, driven solely by external input.
Network synchrony maintenance depends on the balance between input fluctuations and synaptic coupling strength.
Analytical predictions for cascading total firing events show excellent agreement with simulations.

Conclusions:

The study provides a detailed analytical framework for neuronal synchronization in complex networks.
Highlights the distinct roles of external drive and synaptic coupling in network dynamics.
Offers accurate predictions for emergent synchronous firing events in scale-free networks.