Autonomic learning via saturation gain method, and synchronization between neurons

Liu, Zhilong; Zhou, Ping; Ma, Jun; Hobiny, Aatef; Alzahrani, Faris

doi:10.1016/j.chaos.2019.109533

Cited by 33 publications

(3 citation statements)

References 52 publications

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“…As confirmed in Ref. [17], the saturation gain method can be used to adjust the coupling channel to reach complete synchronization between neurons. In fact, it is important to discern the self-adaptive property of synaptic connection, which indicates that the coupling channel should be dependent on the energy diversity between neurons.…”

Section: Scheme and Resultsmentioning

confidence: 87%

Diffusive field coupling-induced synchronization between neural circuits under energy balance

Wang

Sun²,

Ren³

2023

Chinese Phys. B

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When charged bodies come up close to each other, field energy is diffused and their states are regulated under bidirectional field coupling. For biological neurons, the diversity in intrinsic electric and magnetic field energy can create synaptic connection for fast energy balance and synaptic current is passed across the synapse channel; as a result, energy is pumped and exchanged to induce synchronous firing modes. In this letter, a capacitor is used to connect two neural circuits and energy propagation is activated along the coupling channel. The intrinsic field energy in the two neural circuits is exchanged and the coupling intensity is controlled adaptively by using Heaviside function. Some field energy is saved in the coupling channel and then is sent back to the coupled neural circuits for reaching energy balance. Therefore the circuits can reach possible energy balance and complete synchronization. Maybe the diffusive energy of the coupled neural inspires the synaptic connections to grow stronger for possible energy balance.

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Section: Scheme and Resultsmentioning

confidence: 87%

Diffusive field coupling-induced synchronization between neural circuits under energy balance

Wang

Sun²,

Ren³

2023

Chinese Phys. B

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“…From dynamical viewpoint, numerical approach can be used to detect the suitable coupling intensity for stabilizing complete synchronization via bifurcation analysis, calculating the Lyapunov exponents and using saturation gain method. [56] Indeed, the application of master stability function [57][58][59][60] provides helpful guidance to discern the possibility of synchronization control in dynamical systems. In fact, it is more worth investigating the collective behaviors and synchronization stability in memristive neural networks [61][62][63][64] considering the effect of time delay and parameter perturbations by applying feasible schemes.…”

Section: -7mentioning

confidence: 99%

Enhance sensitivity to illumination and synchronization in light-dependent neurons*

Xie

Yao

et al. 2021

Chinese Phys. B

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When a phototube is activated to connect a neural circuit, the output voltage becomes sensitive to external illumination because the photocurrent across the phototube can be controlled by external electromagnetic wave. The channel currents from different branch circuits have different impacts on the outputs voltage of the neural circuit. In this paper, a phototube is incorporated into different branch circuits in a simple neural circuit, and then a light-controlled neuron is obtained for further nonlinear analysis. Indeed, the phototube is considered as exciting source when it is activated by external illumination, and two kinds of light-sensitive neurons are obtained when the phototube is connected to capacitor or induction coil, respectively. Electric synapse coupling is applied to detect possible synchronization between two functional neurons, and the energy consumption along the coupling channel via resistor is estimated. The analog circuits for the two kinds of light-sensitive neurons are supplied for further confirmation by using Multisim. It is found that two light-sensitive neurons and neural circuits can be synchronized by taming the coupling intensity carefully. It provides possible clues to understand the synchronization mechanism for eyes and artificial sensors which are sensitive to illumination. Finally, a section for open problems is supplied for further investigation about its collective behaviors in the network with/without synapse coupling.

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“…The controllability of the coupling channel between nodes is vital for the artificial neuronal network simulation circuit. In order to prevent the coupling strength from increasing before the neurons achieve complete synchronization or phase synchronization, a constant step size must be established while building the neuronal circuit [32,33].…”

Section: Introductionmentioning

confidence: 99%

Approaches for accelerating synchronization between photosensitive neurons in a coupled memory delay system

Zhou,

Huang,

Zhu

et al. 2023

Preprint

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After being stimulated by external light, photoreceptor cells in the retina produce action potentials, which are then transmitted to the visual center to create vision. Reduced vision and even blindness can result from changes in photoreceptor cells. The most cutting-edge treatment available right now is to implant electronic chips to replace the damaged photoreceptor cells. Therefore, it is crucial to understand the biophysical processes that the photoreceptor cell goes through. However, the pertinent biophysical mechanisms are still not entirely understood. This study adds phototube to the FitzHugh Nagumo (FHN) model, builds a light-dependent neuron model by using phototube's photosensitive properties, and proposes a coupled photosensitive neuron model. The effects of synaptic connection between neurons, information transmission delay, external stimulation, electromagnetic effects generated by the memristors, and noise stimulation on the synchronization of the neuronal system are all fully taken into account by the model. The outcomes of the experiment suggest that these variables regulate the speed and stability of synchronization. In an effort to develop a theoretical foundation for the treatment of visual problems, the results of the numerical simulation serve as a certain point of reference for research into the biophysical mechanisms of photosensitive neurons.

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Autonomic learning via saturation gain method, and synchronization between neurons

Cited by 33 publications

References 52 publications

Diffusive field coupling-induced synchronization between neural circuits under energy balance

Diffusive field coupling-induced synchronization between neural circuits under energy balance

Enhance sensitivity to illumination and synchronization in light-dependent neurons*

Approaches for accelerating synchronization between photosensitive neurons in a coupled memory delay system

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