The collective bursting dynamics in a modular neuronal network with synaptic plasticity

Yang, Xiao; Sun, Zhong Kui

doi:10.1007/s11071-017-3606-y

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…Recently, hubs in cultured neuronal networks have been suggested to be involved in propagating spontaneous activity from first-to-fire neurons to the global network ( Schroeter et al, 2015 ). Previous studies have also suggested that the spontaneous bursts of networks are intrinsically related to their modularity ( Moriya et al, 2017 ; Yang et al, 2017 ). Our findings were consistent with those of earlier research.…”

Section: Discussionmentioning

confidence: 98%

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning

Jia

Shao

et al. 2022

Front. Neurosci.

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Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these “learning populations” had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.

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Section: Discussionmentioning

confidence: 98%

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning

Jia

Shao

et al. 2022

Front. Neurosci.

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“…We use the Kuramoto model, which is a well proved paradigm for describing various forms of collective dynamics in a variety of physical, biological, chemical and social systems. Real-life systems have been represented as networks of coupled phase oscillators in studies of synchronization phenomena [29][30][31][32][33][34][35]. We here aim at showing that two key features of real world networks (a scale-free distribution of the weights, and the formation of meso-scale structures) are the results of selforganization under the presence of the above-mentioned mechanisms, homophily, and homeostasis.…”

Section: Model Under Studymentioning

confidence: 99%

Inter-layer competition in adaptive multiplex network

et al. 2018

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We consider competition between layers in adaptive multiplex networks of phase oscillators, where adaptation principles (which cause intra-layer topology evolution) are inspired by real world homophily and homeostasis phenomena. Our model yields the emergence of both scale-free topologies and meso-scale structures in the layers, for an appropriate choice of the control parameters. We further report that the growth of the number of interacting layers leads to a decrease of the global order, due to inter-layer structural competition. However, the increase of the system's scale can effect local synchronization between neighboring (or strongly coupled) nodes. Such unforeseen phenomena is connected with the nature of the competitive mechanism, which implies the rivalry for optimal structure within the whole system, a situation occurring in a variety of natural systems.

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“…[36] Through numerical studies, Yang et al found that an increase in synaptic learning rate slightly inhibits burst synchronization between interacting neurons. [37] In view of the fact that previous works of ISR have been mainly studied in single neurons or simple networks and have not considered the effect of synaptic plasticity, the purpose of this work is to extend the study of ISR to modular neural network. In order to make the network model more physiologically meaningful, synaptic plasticity is also considered in the connections of the modular neural network.…”

Section: Introductionmentioning

confidence: 99%

Inverse stochastic resonance in modular neural network with synaptic plasticity

Yu¹,

Yang²

2023

Chinese Phys. B

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This work explores the inverse stochastic resonance (ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja's synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.

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The collective bursting dynamics in a modular neuronal network with synaptic plasticity

Cited by 8 publications

References 32 publications

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning

Inter-layer competition in adaptive multiplex network

Inverse stochastic resonance in modular neural network with synaptic plasticity

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