Propagation of spiking regularity and double coherence resonance in feedforward networks

Men, Cong; Wang, Jiang; Qin, Ying Mei; Deng, Bin; Tsang, K.M.; Chan, Wai Lok

doi:10.1063/1.3676067

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…Considering the larger proportion of excitatory neurons in the network, it can easily noticed that more excitatory currents are generated with the increase of the synaptic strength than inhibitory currents, which makes the network more active and its firing activities are more easily to be consistent with the frequency of the low-frequency signal. The results are also consistent with the previous investigations where they found the promoting effects of excitatory synapses on the network activities (Bateup et al 2013;Ha ˚nell et al 2015;Men et al 2012).…”

Section: The Effects Of Network Properties On Signal Propagationsupporting

confidence: 93%

“…There are many studies about the interaction effects of two types of neurons based on different network structures. Based on a feedforward network, Men et al found that the optimal firing regularity of neurons in the output layer is decreased with the increase of inhibitory neurons in each layer (Men et al 2012). Kreuz et al verified that coherence resonance of a FitzHugh-Nagumo (FHN) neuron model can be modulated by the balance of excitatory and inhibitory currents in the high-input regime (Kreuz et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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Vibrational resonance in a randomly connected neural network

2018

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A randomly connected network is constructed with similar characteristics (e.g., the ratio of excitatory and inhibitory neurons, the connection probability between neurons, and the axonal conduction delays) as that in the mammalian neocortex and the effects of high-frequency electrical field on the response of the network to a subthreshold low-frequency electrical field are studied in detail. It is found that both the amplitude and frequency of the high-frequency electrical field can modulate the response of the network to the low-frequency electric field. Moreover, vibrational resonance (VR) phenomenon induced by the two types of electrical fields can also be influenced by the network parameters, such as the neuron population, the connection probability between neurons and the synaptic strength. It is interesting that VR is found to be related with the ratio of excitatory neurons that are under high-frequency electrical stimuli. In summary, it is suggested that the interaction of excitatory and inhibitory currents is also an important factor that can influence the performance of VR in neural networks.

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Section: The Effects Of Network Properties On Signal Propagationsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Vibrational resonance in a randomly connected neural network

2018

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“…The phenomenon of enhanced coherence produced by optimal noise has been also described in theoretical models of neurons (Casado, 1997; Neiman et al, 1997; Pikovsky and Kurths, 1997; Lee et al, 1998; Wang et al, 2000; Zhong and Xin, 2000; McMillen and Kopell, 2003; Kreuz et al, 2006; Torcini et al, 2007; Li and Gao, 2008; Guo and Li, 2009; Jiang and Ma, 2009; Franović et al, 2012; Gao and Wang, 2012; Men et al, 2012) and experimentally in physical systems (Lih et al, 1998; Postnov et al, 1998; Giacomelli et al, 2000; Zhong and Xin, 2000, 2001a,b; Calvo et al, 2001; Zhong et al, 2001; Kortlüke et al, 2002; Li and Li, 2003, 2004a,b,c, 2006a,b; Bahar and Moss, 2004; Wang et al, 2010; Xin and Hou, 2006; Perc, 2007; Ermentrout et al, 2008; Li et al, 2008; Perc et al, 2008; Shi and Lang, 2009; Shi and Luo, 2010). It has been experimentally shown that the SR is related to increased local (within one area) and long-range (between different areas) synchrony.…”

Section: Introductionmentioning

confidence: 99%

Enhanced corticomuscular coherence by external stochastic noise

Trenado

Méndez-Balbuena

Manjarrez

et al. 2014

Front. Hum. Neurosci.

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Noise can have beneficial effects as shown by the stochastic resonance (SR) phenomenon which is characterized by performance improvement when an optimal noise is added. Modern attempts to improve human performance utilize this phenomenon. The purpose of the present study was to investigate whether performance improvement by addition of optimum noise (ON) is related to increased cortical motor spectral power (SP) and increased corticomuscular coherence. Eight subjects performed a visuomotor task requiring to compensate with the right index finger a static force (SF) generated by a manipulandum on which Gaussian noise was applied. The finger position was displayed on-line on a monitor as a small white dot which the subjects had to maintain in the center of a green bigger circle. Electroencephalogram from the contralateral motor area, electromyogram from active muscles and finger position were recorded. The performance was measured by the mean absolute deviation (MAD) of the white dot from the zero position. ON compared to the zero noise condition induced an improvement in motor accuracy together with an enhancement of cortical motor SP and corticomuscular coherence in beta-range. These data suggest that the improved sensorimotor performance via SR is consistent with an increase in the cortical motor SP and in the corticomuscular coherence.

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“…For example, the one−dimensional hippocampus was employed to study sequential signal transmission during memory, and the synchronous behaviour was observed in multilayer networks [4][5][6][7]. Some collective activities such as resonance phenomena were studied in FFNs [8,9]. Moreover, strocytes facilitate weak signal detection performance of neuronal systems [10].…”

Section: Introductionmentioning

confidence: 99%

Spike propagation by synchronization and vibrational resonance in feedforward Izhikevich neural network

2022

Preprint

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Multilayer feed − forward neuron networks (FFNs) are the basis of various machine learnings, in which the propagation of neural firing rates with respect to synchronization and vibrational resonance (VR) under white Gaussian noise and high − frequency stimulation (HFS) is important. In this study, the influences of HFS and noise on propagation about synchronous firing rate and the VR are investigated in different kinds of Izhikevich neural FFN. For ten layers excitatory Izhikevich neuron network, it is shown that synchronous firing rates appear gradually and the diverse noise intensities, synaptic weights and time constants take effects on the propagation of synchronous discharge rates. For four layers excitatory FFN, it is observed that the systemic output rates keep smaller than zero, and carries no information of weak signal by using small HFS. The VR phenomenon occurs, when the input of weak low frequency signal (LFS) and signal output keep well phase synchronization, and the LFS is amplified by increasing amplitude of HFS. In the excitatory − inhibition neural multilayer FFN, the propagation by synchronous firing rates is not well, and few inhibition neurons are keep excited. In the systemic output, the synchronization phenomenon can be observed, but not as good as in excitatory neural FFN.

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Propagation of spiking regularity and double coherence resonance in feedforward networks

Cited by 14 publications

References 31 publications

Vibrational resonance in a randomly connected neural network

Vibrational resonance in a randomly connected neural network

Enhanced corticomuscular coherence by external stochastic noise

Spike propagation by synchronization and vibrational resonance in feedforward Izhikevich neural network

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