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Updated: May 22, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
Analysis and dynamic modeling of firing synchronization in electrically interconnected dual-compartment neuronal
Chengji Lu1,2, Longhui Jiang1,2, Qianli Jia1,2
1State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China.
None:
In vitro cultured neuronal networks offer controllable experimental models for investigating neuronal information processing mechanisms and network plasticity. However, research into synchronization and functional connectivity transitions following physical electrical interconnection between isolated compartments remains elusive. This study presents a microsystem that includes a compartmentalized microchamber neuron chip (CMNC) with programmable electrical interconnection and multichannel electrophysiological recording capabilities. The microsystem is utilized to establish artificial electrical interconnection between dual-compartment neuronal networks (DCNNs). We quantitatively evaluated network functional connectivity throughout control, interconnection, and post-disconnection phases, focusing on three key dimensions: spike timing synchrony, firing activity correlation and phase coherence. The experimental data showed that the electrical interconnection had sustained effects on firing synchrony and phase coherence across the DCNNs. After disconnection, synchrony decreased but remained significantly higher than control levels, suggesting a plastic response of the neuronal networks to the electrical coupling. To bridge experimental observations with mechanistic insights, we developed an Electrical-Interconnection Wilson-Cowan Model (EI-WCM), which quantitatively links physical coupling parameters (K) to network-level integration dynamics. The integrated microsystem and dynamical model presented here provide a stable, controllable platform and approach for studying functional connectivity, synergetic interactions and plasticity of neuronal networks, demonstrating significant potential for applications in brain-computer interfaces and neuronal information processing.
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