Formation of local heterogeneity under energy collection in neural networks

Xie, Ying; Yao, Zhao; Ma, Jun

doi:10.1007/s11431-022-2188-2

Cited by 46 publications

(3 citation statements)

References 64 publications

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“…Two recent works [32] claimed that the mode selection has close relation to energy level of the neuron, and then the shift in the energy level accounts for the mode selection in neural activities. When more neurons are clustered in the same region, continuous energy collection [33][34][35][36][37] or release will develop heterogeneity or defects in the local area of the neural network, and energy diversity enables parameter shift for keeping desynchronization between neurons [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Dynamics in a light-sensitive neuron with two capacitive variables

Wang,

Lv,

Zhang

et al. 2024

Phys. Scr.

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Involvement of two capacitive variables into neuron models provides better description of the cell membrane property and then the diversity effect of electromagnetic field inner and outer of the cell membrane can be estimated in clear way. Specific electric components can be combined to build equivalent neural circuits for reproducing similar neural activities under some self-adaptive control schemes. A phototube converts external light into electric stimuli and the injected energy is encoded to excite the cell membranes for presenting suitable firing patterns. Two capacitors are connected via a linear resistor for mimicking the energy exchange and changes of membrane potentials. Combination of memristor into an additive branch circuit of the neural circuit can estimate the effect of electromagnetic induction and energy absorption. The energy function H for this light-sensitive and memristive neuron is calculated in theoretical way, and the average energy function <H> can predict the occurrence of stochastic resonance, which can be confirmed by estimating the distribution of signal to noise ratios. The firing mode is relative to the energy value of the neuron, and a control law is suggested to control the mode transition in neural activities in an adaptive way.

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Section: Introductionmentioning

confidence: 99%

Dynamics in a light-sensitive neuron with two capacitive variables

Wang,

Lv,

Zhang

et al. 2024

Phys. Scr.

Self Cite

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“…[32,41] Collective dynamical behavior is triggered in neuronal networks involving memristive effects, local heterogeneity, and energy flow. [28,[42][43][44][45] To downplay inessential details and emphasize critical features, [46] nonlinear oscillators and circuits are proposed to mimic or reproduce neuron-like behavior and functions. [47] From the viewpoint of biophysical science, the neural response to the combined influence of electrical, magnetic, and mechanical fields is inextricably linked to exchange and transportation of ions between intracellular and extracellular compartments.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Wan 万,

Wu 吴,

Ma 马

et al. 2024

Chinese Phys. B

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Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms. The electrophysiological environment inside and outside of the nerve cell is different. Given the continuous and periodical properties of electromagnetic fields in the cell during its working, electronic components involving two capacitors and a memristor are effective in mimicking the physical features. In this paper, a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance. It is found that the memristive neural circuit can present abundant firing patterns without stimulus. The Hamilton energy function is deduced by using the Helmholtz theorem. Further, the neuronal network consisting of memristive neurons is proposed by introducing energy coupling. The controllability and flexibility of parameters make the model have the ability to describe the dynamics and synchronization behaviors.

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“…In this context, most of these studies on synchronization have been performed by assuming networks of coupled homogeneous oscillators. Only a few have considered networks of coupled oscillators with heterogeneity, a common occurrence in natural systems [41][42][43][44][45][46][47][48][49]. A study conducted by Plotnikov et al [45] revealed that the expansion of heterogeneity within a network of heterogeneous FitzHugh-Nagumo neurons results in improvement of synchrony.…”

Section: Introductionmentioning

confidence: 99%

Synchronization Induced by Layer Mismatch in Multiplex Networks

Anwar

Rakshit

Kurths

et al. 2023

Entropy

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Heterogeneity among interacting units plays an important role in numerous biological and man-made complex systems. While the impacts of heterogeneity on synchronization, in terms of structural mismatch of the layers in multiplex networks, has been studied thoroughly, its influence on intralayer synchronization, in terms of parameter mismatch among the layers, has not been adequately investigated. Here, we study the intralayer synchrony in multiplex networks, where the layers are different from one other, due to parameter mismatch in their local dynamics. In such a multiplex network, the intralayer coupling strength for the emergence of intralayer synchronization decreases upon the introduction of impurity among the layers, which is caused by a parameter mismatch in their local dynamics. Furthermore, the area of occurrence of intralayer synchronization also widens with increasing mismatch. We analytically derive a condition under which the intralayer synchronous solution exists, and we even sustain its stability. We also prove that, in spite of the mismatch among the layers, all the layers of the multiplex network synchronize simultaneously. Our results indicate that a multiplex network with mismatched layers can induce synchrony more easily than a multiplex network with identical layers.

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Formation of local heterogeneity under energy collection in neural networks

Cited by 46 publications

References 64 publications

Dynamics in a light-sensitive neuron with two capacitive variables

Dynamics in a light-sensitive neuron with two capacitive variables

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Synchronization Induced by Layer Mismatch in Multiplex Networks

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