Formation and maintenance of neuronal collective dynamics through local perturbation and intrinsic node dynamics

Runzhou Liu, Yan Zhang, Jijun Wang, Muhua Zheng and Kesheng Xu

Chaos, Solitons & Fractals, 2025, vol. 199, issue P1

Abstract: Neuronal assemblies have primarily been examined through synaptic weights and plasticity to understand variability in response timing during local perturbations in various experimental studies. However, an important unresolved question is how local perturbations, which disrupt the excitatory–inhibitory balance, potentially trigger widespread changes in spiking variability. Our findings show that the variability of spiking patterns depends on the presence of perturbations and synaptic weights in the neural networks built with chaotic and nonchaotic cells. Without perturbations, the excitatory population exhibits firing patterns resembling isolated neurons. Conversely, when perturbations occur, unperturbed excitatory neurons display symmetry latency firing patterns with smaller perturbation amplitude and demonstrate full or clustered synchronization at higher perturbation amplitudes, both influenced by the intrinsic node dynamics. These findings highlight that the separation of firing rates and the intrinsic node dynamics are essential factors in the emergence of diverse firing patterns. This investigation underscores the significance of intrinsic node dynamics in neuronal networks and the role of external perturbations in forming neuronal assemblies.

Keywords: Pattern formation; Local perturbations; Intrinsic node dynamics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:199:y:2025:i:p1:s0960077925006617

DOI: 10.1016/j.chaos.2025.116648

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