Cooperative effect of random and time-periodic coupling strength on synchronization transitions in one-way coupled neural system: mean field approach

Jian-Cheng, Shi; Luo, Maokang; Chu-sheng, Huang

doi:10.1007/s11571-017-9437-1

Cited by 10 publications

(3 citation statements)

References 44 publications

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“…Neuronal bursting, a common firing pattern of these electrical activities observed in many electrophysiological experiments, has been studied extensively (Ji et al 2015 ; Izhikevich 2000 ; Kepecs and Wang 2000 ; Wang et al 2011 ; Shi et al 2017 ; Zhang et al 2016 ; Perc and Marhl 2005 ; Gu et al 2015 ; Jia et al 2017 ; Duan et al 2017 ). These studies mainly focus either on mathematical models for numerical analysis or on elucidating the time coding characteristics of bursting activities (Ji et al 2015 ; Izhikevich 2000 ; Kepecs and Wang 2000 ; Wang et al 2011 ; Shi et al 2017 ). The latter approach is more common because it is assumed that the coding information is contained in the precise time structure between spikes or between bursts (Zhang et al 2016 ; Perc and Marhl 2005 ; Gu et al 2015 ; Jia et al 2017 ; Duan et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Energy expenditure computation of a single bursting neuron

et al. 2018

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Brief bursts of high-frequency spikes are a common firing pattern of neurons. The cellular mechanisms of bursting and its biological significance remain a matter of debate. Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting. The flow of ions across the membrane of the bursting neuron with or without current stimulation and its power which contributes to the change of the transmembrane electrical potential energy are analyzed here in detail. We find that during the depolarization of spikes in bursting this power becomes negative, which was also discovered in previous research with another energy model. We also find that the neuron’s energy consumption during bursting is minimal. Especially in the spontaneous state without stimulation, the total energy consumption (2.152 × 10−7 J) during 30 s of bursting is very similar to the biological energy consumption (2.468 × 10−7 J) during the generation of a single action potential, as shown in Wang et al. (Neural Plast 2017, 2017a). Our results suggest that this property of low energy consumption could simply be the consequence of the biophysics of generating bursts, which is consistent with the principle of energy minimization. Our results also imply that neural energy plays a critical role in neural coding, which opens a new avenue for research of a central challenge facing neuroscience today.

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Section: Introductionmentioning

confidence: 99%

Energy expenditure computation of a single bursting neuron

et al. 2018

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“…(4) Since the energy is a scalar, whether it is a single or neuron population, or whether it is a network or a behavioral, as well as a linear or a nonlinear neural model, their dynamic responses can be used to describe patterns of energy coding by superposition of neural energy [4,[6][7][8][9][10][11][12]. Thus, global information about the inherent, intrinsic, and functional neural activities can be obtained, while it cannot be achieved by other traditional coding theories [15,[17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

The essence of neuronal activity from the consistency of two different neuron models

Wang

Zhu

2018

Nonlinear Dyn

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Based on the H-H equation, this study has proposed the calculation and analysis of energy expenditure for a single neuron which is activated at supthreshold and subthreshold, as well as the criterion of the energy expenditure of neurons activated supthreshold and subthreshold, which was the maximum power of a sodium ion pump. Results of the study showed that not only the electrophysiological activities of neurons were strictly restricted by the energy levels in the brain, but also the activities of neurons also had dual nature, meaning that subthreshold neurons were mainly with energy expenditure, while sup-threshold neurons were with both energy absorption and energy expenditure. These new findings were compared with the novel neuro-biophysical models that we have published last year, uncovering that the two models were essentially equivalent.

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“…(1) Voltage Synchrony Index. The degree of synchrony in the network is calculated according to the method developed by [42][43][44][45]. The population averaged voltage VðtÞ is defined as VðtÞ = ðV 1 ðtÞ+⋯+V N ðtÞÞ/N, where N is the number of cells, and then the voltage synchrony index SV is computed as follows:…”

Section: Description Of the Input From Differentmentioning

confidence: 99%

Interaction of Indirect and Hyperdirect Pathways on Synchrony and Tremor-Related Oscillation in the Basal Ganglia

Shi

Wang

2021

Neural Plasticity

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Low-frequency oscillatory activity (3-9 Hz) and increased synchrony in the basal ganglia (BG) are recognized to be crucial for Parkinsonian tremor. However, the dynamical mechanism underlying the tremor-related oscillations still remains unknown. In this paper, the roles of the indirect and hyperdirect pathways on synchronization and tremor-related oscillations are considered based on a modified Hodgkin-Huxley model. Firstly, the effects of indirect and hyperdirect pathways are analysed individually, which show that increased striatal activity to the globus pallidus external (GPe) or strong cortical gamma input to the subthalamic nucleus (STN) is sufficient to promote synchrony and tremor-related oscillations in the BG network. Then, the mutual effects of both pathways are analysed by adjusting the related currents simultaneously. Our results suggest that synchrony and tremor-related oscillations would be strengthened if the current of these two paths are in relative imbalance. And the network tends to be less synchronized and less tremulous when the frequency of cortical input is in the theta band. These findings may provide novel treatments in the cortex and striatum to alleviate symptoms of tremor in Parkinson’s disease.

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Cooperative effect of random and time-periodic coupling strength on synchronization transitions in one-way coupled neural system: mean field approach

Cited by 10 publications

References 44 publications

Energy expenditure computation of a single bursting neuron

Energy expenditure computation of a single bursting neuron

The essence of neuronal activity from the consistency of two different neuron models

Interaction of Indirect and Hyperdirect Pathways on Synchrony and Tremor-Related Oscillation in the Basal Ganglia

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