Regulation of Synaptic Plasticity and Adaptive Convergence Under Rhythmic Stimulation of an In Vitro Hippocampal Neuronal Network of Cultured Cells

Synaptic plasticity constitutes a fundamental mechanism of neural systems. Rhythmic activities (e.g., θ and γ oscillations) play a critical role in modulating network plasticity efficiency in biological neural systems. However, the rules governing plasticity and adaptive regulation of in vitro cultured networks under structured electrical stimulation remain insufficiently characterized. To quantitatively investigate these regulatory effects within a highly controlled and low-interference environment, we utilized primary mice hippocampal neurons cultured on multielectrode arrays (MEAs) and executed two dedicated sets of experiments. (1) Spatiotemporal electrical stimulation paradigms, combined with connectivity analysis, revealed pronounced regulation effects of network plasticity. (2) Physiologically inspired rhythmic stimulation (θ: 7.8 Hz, γ: 40 Hz) with varying pulse repetitions was then applied. Although both rhythms induced distinct frequency-dependent plasticity modulation, the disparity between their modulatory effects progressively diminished with increasing stimulation pulse numbers, suggesting an intrinsic adaptive regulatory mechanism. Collectively, our findings characterize the effects of plasticity regulation and reveal the mechanisms underlying adaptive convergence in in vitro neuronal systems. These results advance the understanding of network plasticity, providing a technical foundation for functional shaping and modulation of in vitro neural networks while supporting future explorations into learning-oriented modulation.

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Publication Details

Journal
Biosensors
Published
2026-01-19
DOI
https://doi.org/10.3390/bios16010065
Primary Topic
Neuroscience and Neural Engineering
Type
article
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article

Regulation of Synaptic Plasticity and Adaptive Convergence Under Rhythmic Stimulation of an In Vitro Hippocampal Neuronal Network of Cultured Cells

Chengji Lu, Mixia Wang, Shutong Sun, Xinxia Cai et al.
Biosensors
Neuroscience and Neural Engineering
article

Regulation of Synaptic Plasticity and Adaptive Convergence Under Rhythmic Stimulation of an In Vitro Hippocampal Neuronal Network of Cultured Cells

Chengji Lu, Mixia Wang, Shutong Sun, Xinxia Cai, Longhui Jiang, Kui Zhang, Li Shang, Shangchen Li, Yaoyao Liu, Jinping Luo
article en

Abstract

Synaptic plasticity constitutes a fundamental mechanism of neural systems. Rhythmic activities (e.g., θ and γ oscillations) play a critical role in modulating network plasticity efficiency in biological neural systems. However, the rules governing plasticity and adaptive regulation of in vitro cultured networks under structured electrical stimulation remain insufficiently characterized. To quantitatively investigate these regulatory effects within a highly controlled and low-interference environment, we utilized primary mice hippocampal neurons cultured on multielectrode arrays (MEAs) and executed two dedicated sets of experiments. (1) Spatiotemporal electrical stimulation paradigms, combined with connectivity analysis, revealed pronounced regulation effects of network plasticity. (2) Physiologically inspired rhythmic stimulation (θ: 7.8 Hz, γ: 40 Hz) with varying pulse repetitions was then applied. Although both rhythms induced distinct frequency-dependent plasticity modulation, the disparity between their modulatory effects progressively diminished with increasing stimulation pulse numbers, suggesting an intrinsic adaptive regulatory mechanism. Collectively, our findings characterize the effects of plasticity regulation and reveal the mechanisms underlying adaptive convergence in in vitro neuronal systems. These results advance the understanding of network plasticity, providing a technical foundation for functional shaping and modulation of in vitro neural networks while supporting future explorations into learning-oriented modulation.

BiosensorsVol. 16(1)
Chinese Academy of Sciences (CN), State Key Laboratory of Transducer Technology (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 85%
Neuroscience and Neural Engineering
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