Transmission and detection of biharmonic envelope signal in a feed-forward multilayer neural network

Yao, Chenggui; Ma, Jun; Zhang, He; Qian, Yu; Liu, Liping

doi:10.1016/j.physa.2019.02.053

Cited by 22 publications

(7 citation statements)

References 37 publications

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“…In another conceptual study, the notions of ‘perception’ and ‘propagation’ in FFN structures have been underlined, focusing on their beneficial role in the appearance of VR in a neuronal medium [86]. To this end, Yao et al .…”

Section: Stochastic Resonance and Vibrational Resonance In Complex Neuronal Network With Different Topologiesmentioning

confidence: 99%

“…Considering the cases with and without noise contribution ( b ), they showed that the presence of noise can enhance propagation of a weak signal detected by VR mechanism (blue empty circles) and the noise-free case prevents detection and transmission of the signal under the same conditions (green filled circles). Figure is reproduced with permission from [86]. (Online version in colour.)…”

Section: Stochastic Resonance and Vibrational Resonance In Complex Neuronal Network With Different Topologiesmentioning

confidence: 99%

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Stochastic and vibrational resonance in complex networks of neurons

Çalım

Palabaş

Uzuntarla

2021

Phil. Trans. R. Soc. A.

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The concept of resonance in nonlinear systems is crucial and traditionally refers to a specific realization of maximum response provoked by a particular external perturbation. Depending on the system and the nature of perturbation, many different resonance types have been identified in various fields of science. A prominent example is in neuroscience where it has been widely accepted that a neural system may exhibit resonances at microscopic, mesoscopic and macroscopic scales and benefit from such resonances in various tasks. In this context, the two well-known forms are stochastic and vibrational resonance phenomena which manifest that detection and propagation of a feeble information signal in neural structures can be enhanced by additional perturbations via these two resonance mechanisms. Given the importance of network architecture in proper functioning of the nervous system, we here present a review of recent studies on stochastic and vibrational resonance phenomena in neuronal media, focusing mainly on their emergence in complex networks of neurons as well as in simple network structures that represent local behaviours of neuron communities. From this perspective, we aim to provide a secure guide by including theoretical and experimental approaches that analyse in detail possible reasons and necessary conditions for the appearance of stochastic resonance and vibrational resonance in neural systems. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 2)’.

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Section: Stochastic Resonance and Vibrational Resonance In Complex Neuronal Network With Different Topologiesmentioning

confidence: 99%

Section: Stochastic Resonance and Vibrational Resonance In Complex Neuronal Network With Different Topologiesmentioning

confidence: 99%

Stochastic and vibrational resonance in complex networks of neurons

Çalım

Palabaş

Uzuntarla

2021

Phil. Trans. R. Soc. A.

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“…After that, it was widely used to describe the phenomenon that the presence of internal noise or external noise in nonlinear system can increase the response of the system output [6]. In the process of signal analysis, noise is often regarded as a nuisance, because the presence of noise reduces the signal-to-noise ratio and affects the extraction of useful information [7,8]. However, in some specific nonlinear systems, the presence of noise can enhance the detection ability of weak signals [9,10].…”

Section: Introductionmentioning

confidence: 99%

Chaotic resonance in Izhikevich neural network motifs under electromagnetic induction

et al. 2022

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Chaotic resonance (CR) is the response of a nonlinear system to weak signals enhanced by internal or external chaotic activity (such as the signal derived from Lorenz system). In this paper, the triple-neuron feed-forward loop (FFL) Izhikevich neural network motifs with eight types are constructed as the nonlinear systems, and the effects of EMI on CR phenomenon in FFL neuronal network motifs are studied. It is found that both the single Izhikevich neural model under electromagnetic induction (EMI) and its network motifs exhibit CR phenomenon depending on the chaotic current intensity. There exists an optimal chaotic current intensity ensuring the best detection of weak signal in single Izhikevich neuron or its network motifs via CR. The EMI can enhance the ability of neuron to detect weak signals. For T1-FFL and T2-FFL motifs, the adjustment of EMI parameters makes T2-FFL show a more obvious CR phenomenon than that for T1-FFL motifs, which is different from the impact of system parameters (e.g., the weak signal frequency, the coupling strength, and the time delay) on CR. Another interesting phenomenon is that the variation of CR with time delay exhibits quasi periodic characteristics. Our results showed that CR effect is a robust phenomenon which is observed in both single Izhikevich neuron and network motifs, which might help one understand how to improve the ability of weak signal detection and propagation in neuronal system.

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“…Following its introduction by Landa & McClintock [23] and motivated by the aforementioned potential applications, VR has now been demonstrated and analysed in a diversity of model systems theoretically, numerically and experimentally, cutting across many fields such as neuroscience, plasma physics, laser physics, acoustics and engineering. Specifically, the VR phenomenon has been investigated in bistable systems [24,28–35], multistable systems [36,37], ratchet devices [38], excitable systems [39], quintic oscillators [40–44], coupled oscillators [25,45–47], overdamped systems [30], delayed dynamical systems [44,46,48–52], asymmetric Duffing oscillators [53], fractional order oscillators [32,42,54], neural models [39,50,55–61], oscillatory networks [46,55,58–60,62–64], biological nonlinear systems [49,61,64], parametrically excited systems [34,65–69], systems with nonlinear damping [37,70–72], and deformed potential [73], disordered systems [74], quantum systems [75,76], as well as harmonically trapped and roughed potentials [72,77]. More importantly, VR has been demonstrated in experimental realizations, especially in multistable systems, arrays of hard limiters, bistable vertical-cavity surface-emitting lasers [28,29,33,78–81] and Chua circuits [82,83].…”

Section: Introductionmentioning

confidence: 99%

Vibrational resonances in driven oscillators with position-dependent mass

Roy-Layinde

Vincent

Abolade

et al. 2021

Phil. Trans. R. Soc. A.

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The vibrational resonance (VR) phenomenon has received a great deal of research attention over the two decades since its introduction. The wide range of theoretical and experimental results obtained has, however, been confined to VR in systems with constant mass. We now extend the VR formalism to encompass systems with position-dependent mass (PDM). We consider a generalized classical counterpart of the quantum mechanical nonlinear oscillator with PDM. By developing a theoretical framework for determining the response amplitude of PDM systems, we examine and analyse their VR phenomenona, obtain conditions for the occurrence of resonances, show that the role played by PDM can be both inductive and contributory, and suggest that PDM effects could usefully be explored to maximize the efficiency of devices being operated in VR modes. Our analysis suggests new directions for the investigation of VR in a general class of PDM systems. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

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Transmission and detection of biharmonic envelope signal in a feed-forward multilayer neural network

Cited by 22 publications

References 37 publications

Stochastic and vibrational resonance in complex networks of neurons

Stochastic and vibrational resonance in complex networks of neurons

Chaotic resonance in Izhikevich neural network motifs under electromagnetic induction

Vibrational resonances in driven oscillators with position-dependent mass

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