Stochastic resonance on Newman–Watts networks of Hodgkin–Huxley neurons with local periodic driving

Özer, Mahmut; Perc, Matjaž; Uzuntarla, Muhammet

doi:10.1016/j.physleta.2009.01.034

Cited by 181 publications

(70 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, the stochastic resonance on Newman-Watts networks 21 of Hodgkin-Huxley neurons could be amplified via fine-tuning of the small-world network structure, depending significantly on the coupling strengths among neurons and the driving frequency of the pacemaker. 22 Coherence resonance on Watts-Strogatz smallworld Hodgkin-Huxley neuronal networks has also been investigated, and it was found that increasing the randomness of the network topology leads to enhancement of temporal coherence. 23 Furthermore, Kwon et al 24 showed that the coherence resonance can be considerably improved by adding a small fraction of long-range connections for an intermediate coupling strength in a Watts-Strogatz small-world neuronal network with spatially correlated noise input.…”

Section: Introductionmentioning

confidence: 99%

Delay-induced multiple stochastic resonances on scale-free neuronal networks

Wang

Perc

Duan

et al. 2009

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

240

View full text Add to dashboard Cite

We study the effects of periodic subthreshold pacemaker activity and time-delayed coupling on stochastic resonance over scale-free neuronal networks. As the two extreme options, we introduce the pacemaker respectively to the neuron with the highest degree and to one of the neurons with the lowest degree within the network, but we also consider the case when all neurons are exposed to the periodic forcing. In the absence of delay, we show that an intermediate intensity of noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble, irrespective to its placing, thus providing evidences for stochastic resonance on the scale-free neuronal networks. Interestingly thereby, if the forcing in form of a periodic pulse train is introduced to all neurons forming the network, the stochastic resonance decreases as compared to the case when only a single neuron is paced. Moreover, we show that finite delays in coupling can significantly affect the stochastic resonance on scale-free neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances independently of the placing of the pacemaker, but they can also altogether destroy stochastic resonance. Delay-induced multiple stochastic resonances manifest as well-expressed maxima of the correlation measure, appearing at every multiple of the pacemaker period. We argue that fine-tuned delays and locally active pacemakers are vital for assuring optimal conditions for stochastic resonance on complex neuronal networks. It is well known that noise can play a constructive role in different types of nonlinear dynamical systems, and stochastic resonance is perhaps the most prominent example of this fact. The objective of this article is to extend the scope of stochastic resonance to complex networks, whereby the deterministic periodic input is not only limited in its strength but also its outreach. More precisely, scale-free neuronal networks are studied on which the subthreshold periodic forcing is introduced only to a single neuron of the network, thus acting as a pacemaker. We want to determine to what extent the complex scale-free topology can aid the pacemaker to entrain the complete neuronal ensemble with the help of fine-tuned additive noise. Moreover, the new findings are compared with results obtained via the more traditional setup where every neuron of the network is subjected to a weak periodic forcing. It is found that scale-free topologies are very efficient in propagating noise-supported localized weak rhythmic activities. Also, it is found that these paced networks are superior to globally forced networks in that stochastic resonance is better expressed on the former. Importantly, since time delays are inherent to the nervous system we take this explicitly into account via time-delayed coupling. We report on the occurrence of delay-induced multiple stochastic resonances on scale-free neuronal networks, which appear due to the locking between the delay length and the oscillation period of the pacemaker.

show abstract

Section: Introductionmentioning

confidence: 99%

Delay-induced multiple stochastic resonances on scale-free neuronal networks

Wang

Perc

Duan

et al. 2009

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

240

View full text Add to dashboard Cite

show abstract

“…In previous works, we found that spatial synchronization and temporal coherence, which are practically absent in regular networks, can be greatly enhanced by random shortcuts between the neurons [18][19][20]. Stochastic resonance on small-world neuronal networks via a pacemaker was also investigated by Perc [21,22], and Kwon [23] reported that coherence resonance can be considerably improved by small-world connectivity in networks of HH neurons. Moreover, spatial synchronization and stochastic resonance have also been extensively studied on scale-free networks [24][25][26][27].…”

Section: Introductionmentioning

confidence: 94%

Spatiotemporal dynamics on small-world neuronal networks: The roles of two types of time-delayed coupling

Jiang

Hou

2011

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

We investigate temporal coherence and spatial synchronization on small-world networks consisting of noisy Terman-Wang (TW) excitable neurons in dependence on two types of time-delayed coupling:For the former case, we show that time delay in the coupling can dramatically enhance temporal coherence and spatial synchrony of the noise-induced spike trains. In addition, if the delay time τ is tuned to nearly match the intrinsic spike period of the neuronal network, the system dynamics reaches a most ordered state, which is both periodic in time and nearly synchronized in space, demonstrating an interesting resonance phenomenon with delay. For the latter case, however, we can not achieve a similar spatiotemporal ordered state, but the neuronal dynamics exhibits interesting synchronization transition with time delay from zigzag fronts of excitations to dynamic clustering anti-phase synchronization (APS), and further to clustered chimera states which have spatially distributed anti-phase coherence separated by incoherence. Furthermore, we also show how these findings are influenced by the change of the noise intensity and the rewiring probability. Finally, qualitative analysis is given to illustrate the numerical results. * Electronic address: hzhlj@ustc.edu.cn

show abstract

“…Modeling the dynamic behavior of a network has attracted much attention in recent years [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Gibson et al [6] showed by paired-cell recordings that while the same type of inhibitory neurons were strongly interconnected by electrical synapses, electrical synapses between different inhibitory cell types were rare.…”

Section: Introductionmentioning

confidence: 99%

Modeling of time delay-induced multiple synchronization behavior of interneuronal networks with the Izhikevich neuron model

Cakir¹

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:The synchronization behavior of the networks of fast-spiking interneurons is investigated by using one of the phenomenological neural network models, the Izhikevich model. Since electrical and chemical synapses exist within the same networks of inhibitory cells, delayed inhibitory and fast electrical synapses are coupled in the simulations. The effects of hybrid synapses in promoting synchronous activity in neural networks are investigated with short and long time delays. The distinct frequency bands observed in electroencephalography and magnetoencephalography signals are determined using the raster plots of neural networks. For quantitative comparison of activities in networks, the degree of synchrony in the network is calculated. The influences of several network parameters such as inhibitory synaptic strength, electrical synaptic strength, synaptic time constant, and time delay on the network activity are investigated.It is observed that the coupling of electrical and chemical synapses promotes multiple synchronous behaviors in the network. Another important finding is that even though the synchronization measure is highly dependent on inhibitory synaptic strengths at low electrical synaptic strength and synaptic time constant, not much dependence on inhibitory synaptic strengths is observed at high electrical synaptic strength.

show abstract

Stochastic resonance on Newman–Watts networks of Hodgkin–Huxley neurons with local periodic driving

Cited by 181 publications

References 65 publications

Delay-induced multiple stochastic resonances on scale-free neuronal networks

Delay-induced multiple stochastic resonances on scale-free neuronal networks

Spatiotemporal dynamics on small-world neuronal networks: The roles of two types of time-delayed coupling

Modeling of time delay-induced multiple synchronization behavior of interneuronal networks with the Izhikevich neuron model

Contact Info

Product

Resources

About