Double inverse stochastic resonance with dynamic synapses

Uzuntarla, Muhammet; Torres, Joaquín J.; So, Paul; Özer, Mahmut; Barreto, Ernest

doi:10.1103/physreve.95.012404

Cited by 47 publications

(22 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During ISR, when the intensity of the (weak) noise amplitude is increased, it will first strongly inhibit the spiking activity and decrease the mean number of spikes to a minimal value, but a further increase in the noise amplitude will increase the mean number of spikes again, even above what is observed in the noise-free case. Mathematically, ISR has not been extensively studied as compared to other resonance phenomena; see [50][51][52][53], and it was recently confirmed experimentally in [54].…”

Section: Introductionmentioning

confidence: 99%

Coherent neural oscillations induced by weak synaptic noise

Yamakou

Jost

2018

Nonlinear Dyn

View full text Add to dashboard Cite

We analyze the effect of synaptic noise on the dynamics of the FitzHugh-Nagumo (FHN) neuron model. In our deterministic parameter regime, a limit cycle solution cannot emerge through a singular Hopf bifurcation, but such a limit cycle can nevertheless arise as a stochastic effect, as a consequence of weak synaptic noise in a regime of strong timescale separation (ε → 0) between the slow and fast variables of the model. We investigate the mechanism behind this phenomenon, known as self-induced stochastic resonance (SISR) (Muratov et al. in Physica D 210:227-240, 2005), by using multiple-time perturbation techniques and by analyzing the escape mechanism of the random trajectories from the stable manifolds of the model equation. Even though SISR occurs only in the limit as the singular parameter ε → 0, decreasing ε does not increase the coherence of the oscillations in the FHN model, but rather increases the interval of the noise amplitude σ for which coherence occurs. This is in contrast to the dynamical system studied in Mura- 2005). Moreover, the phenomenon is robust under parameter tuning. Numerical simulations exhibit the results predicted by the theoretical analysis. In fact, our analysis together with that in Yamakou and Jost (Weak noise-induced transitions with inhibition and modulation of neural oscillations, 2017) reveals that the FHN model can support different stochastic resonance phenomena. While it had been known (Pikovsky and Kurths in Phys Rev Lett 78:775-778, 1997) that coherence resonance can occur when the slow variable is subjected to noise, we show that, when noise is added to the fast variable, two other types, inverse stochastic resonance and SISR, may emerge in the same weak noise limit and that the transition between these phenomena can be induced by varying a simple parameter.

show abstract

Section: Introductionmentioning

confidence: 99%

Coherent neural oscillations induced by weak synaptic noise

Yamakou

Jost

2018

Nonlinear Dyn

View full text Add to dashboard Cite

show abstract

“…These are separated by a boundary, which is mediated by an unstable limit cycle. (For a more detailed description of the HH model bifurcation diagram, see for instance [19, 20]). Notably, the minimum distance from the stable equilibrium to this boundary is much larger than the minimum distance between the boundary and the stable limit cycle.…”

Section: Resultsmentioning

confidence: 99%

“…ISR is a recently-discovered phenomenon that has been investigated both theoretically and experimentally at the level of a single neuron [12–15, 18–20, 46]. In those studies, the average spiking rate of the neuron was calculated over many trials with the same neuron.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Inverse stochastic resonance in networks of spiking neurons

2017

Self Cite

View full text Add to dashboard Cite

Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron’s intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

show abstract

“…In the field of signal processing, stochastic resonance can transfer the energy of some noise signals to useful signals, which is beneficial to the identification of weak signals submerged in noise [30][31][32]. The noise signal is input to the bistable or multistable nonlinear system.…”

Section: Stochastic Resonancementioning

confidence: 99%

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Chen

2017

International Journal of Rotating Machinery

View full text Add to dashboard Cite

Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

show abstract

Double inverse stochastic resonance with dynamic synapses

Cited by 47 publications

References 62 publications

Coherent neural oscillations induced by weak synaptic noise

Coherent neural oscillations induced by weak synaptic noise

Inverse stochastic resonance in networks of spiking neurons

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Contact Info

Product

Resources

About