Effects of chaotic activity and time delay on signal transmission in FitzHugh-Nagumo neuronal system

Yu, Dayang; Zhou, Xiuying; Wang, Guowei; Ding, Qianming; Li, Tianyu; Jia, Ya

doi:10.1007/s11571-021-09743-5

Cited by 28 publications

(5 citation statements)

References 74 publications

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“…We consider a coupled neuron-astrocyte pair with noise where the dynamics of the neuron is modeled with Fitzhugh-Nagumo (FHN) equations as follows 50,51 :…”

Section: Models and Methodsmentioning

confidence: 99%

Controlling the spontaneous firing behavior of a neuron with astrocyte

Palabas,

Longtin,

Ghosh

et al. 2022

Preprint

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Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocyte on the spontaneous firing activity of a neuron through a combined model which includes a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size.Astrocytes are the most numerous glial cells in the mature brain, and often surround neuronal somata and dendrites and provide fine enmeshment of synapses. Unlike neurons, these star-shaped cells do not elicit action potentials, yet they contribute to information processing via feedback to the cells by listening to the synaptic activity. We here investigate the their role on shaping the spontaneous firing behavior of a neuron. By using a neuron-astrocyte pair model, we identify distinct astrocyte-controlled neural activity patterns and explore their individual statistical characteristics under the variation of different biologicallyplausible intrinsic astrocytic factors.

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“…We consider a coupled neuron-astrocyte pair with noise where the dynamics of the neuron is modeled with Fitzhugh-Nagumo (FHN) equations as follows 50,51 :…”

Section: Models and Methodsmentioning

confidence: 99%

Controlling the spontaneous firing behavior of a neuron with astrocyte

Palabas,

Longtin,

Ghosh

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…But in the past several decades, the stereotypical opinion has been dramatically altered by many novel findings in systems far from equilibrium [1]. Among them stochastic resonance (SR), as a cooperative but anti-intuitive effect between noise of suitable level and weak coherent signal under certain nonlinear circumstance [2][3][4][5][6], has become dramatically famous. It has been revealed that the phenomenon of SR can occur in various nonlinear systems including underdamped bistable Duffing oscillator [7], Josephson junctions [8,9] and neuron models [10][11][12][13]etc.…”

Section: Introductionmentioning

confidence: 99%

Novel classification algorithms inspired by firing rate stochastic resonance

Xu,

Fu,

Mei

et al. 2024

Preprint

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The aim of this paper is to present a category of novel pattern classification algorithms inspired by the phenomenon of the firing rate based stochastic resonance (SR) in a noisy leaky integrate-and-fire neuron. To this end, the firing rate-based SR phenomenon in the noisy leaky integrate-and-fire neuron model is displayed by means of the approximation of adiabatic elimination. And then, a multi-layer neural network with back-propagation learning is constructed by using the stationary firing rare for activation function. Since the intensity of the involving Gaussian white noise is taken as an independent trainable parameter, the benefit of noise can be maximally utilized. The algorithm and its improvements have been verified with binary classification and handwritten digit recognition. By further simplifying calculation of the firing rate activation, this algorithm is embedded into the network architecture of PreAct-ResNet-18 for more complex tasks. It is shown that the improved version based on the stochastic gradient descent (SGD) optimizer outperforms several typical artificial neural network algorithms and brain-inspired spiking neural network (SNN) algorithms on the CIFAR-10 dataset, and it achieves a good accuracy on CIFAR-100, surpassing the accuracy based on the ReLU activation. Since the trained intensity of Gaussian white noise is nonzero in all the applications, stochastic resonance like effect has been observed. Hence it is disclosed from this study that noise can really be designed as an optimizable factor into the brain-inspired machine learning algorithms.

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“…DNA strand displacement (DSD) has garnered significant attention from both biochemists and mathematicians due to its meaningful attributes and potential applications over the past few years [1][2][3][4][5]. As a molecular nanotechnology, DSD provides a powerful biological programming language to describe and analyze nonlinear systems [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Backstepping synchronization control for four-dimensional chaotic system based on DNA strand displacement

Sun,

Wang,

Shan

et al. 2024

Phys. Scr.

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Backstepping control is an important nonlinear control design method, which realizes the control of complex systems by constructing control law step by step, and has significant advantages for dealing with complex nonlinear systems. This article proposes a synchronization technique for four-dimensional chaotic systems using a combination of backstepping control method and DNA strand displacement technology. By relying on theoretical knowledge of DNA molecules, five basic chemical reaction modules such as trigger reaction, reference reaction, catalytic reaction, annihilation reaction and degradation reaction are given to construct a four-dimensional DNA chaotic system. On the basis of the relevant theory of chaotic dynamics, the constructed system is analyzed by Lyapunov exponent diagram and spectral entropy complexity algorithm, and the results come to the conclusion that the system reveals extremely complex and varied dynamic behaviors. Combining DNA strand displacement technology with backstepping control method, four controllers are developed to ensure that the trajectories of two homogeneous chaotic systems are synchronized. The numerical simulation results validate the feasibility and applicability of the proposed method. The method proposed in this paper may provide some references in the field of DNA molecular chaos synchronization control.

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Effects of chaotic activity and time delay on signal transmission in FitzHugh-Nagumo neuronal system

Cited by 28 publications

References 74 publications

Controlling the spontaneous firing behavior of a neuron with astrocyte

Controlling the spontaneous firing behavior of a neuron with astrocyte

Novel classification algorithms inspired by firing rate stochastic resonance

Backstepping synchronization control for four-dimensional chaotic system based on DNA strand displacement

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