Coherence resonance in an autaptic Hodgkin–Huxley neuron with time delay

Song, Xiaoqiu; Wang, Hengtong; Chen, Yong

doi:10.1007/s11071-018-4349-0

Cited by 37 publications

(8 citation statements)

References 55 publications

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“…Izhikevich neural model is a neuronal discharge model proposed by Eugune M. Izhikevich [48] in 2004, which combines the advantages of Integrate-and Fire (IF) neuronal model [49] and HH neuronal model [50]. It is not only close to the discharge characteristics of real biological neurons, but also convenient for large-scale simulation [51].…”

Section: Model Description 21 Izhikevich Neural Model In the Presence...mentioning

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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Section: Model Description 21 Izhikevich Neural Model In the Presence...mentioning

confidence: 99%

Chaotic resonance in Izhikevich neural network motifs under electromagnetic induction

et al. 2022

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“…In biological experiment, inhibitory auatpse can suppress action potential [18] and induce enhanced precision of spike timing, [6] and excitatory autapse can induce persistent firing and enhancement of bursting. [7,19] In the theoretical models, autapse has been identified to play important roles in modulating firing pattern transitions, [20][21][22][23] stochastic dynamics, [24][25][26][27] and spatiotemporal dynamics of networks. [28][29][30][31][32][33] In addition to these normal phenomena, the paradoxical phenomenon for both inhibitory and excitatory autapses are acquired.…”

Section: Introductionmentioning

confidence: 99%

Delayed excitatory self-feedback-induced negative responses of complex neuronal bursting patterns*

Ben

2021

Chinese Phys. B

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In traditional viewpoint, excitatory modulation always promotes neural firing activities. On contrary, the negative responses of complex bursting behaviors to excitatory self-feedback mediated by autapse with time delay are acquired in the present paper. Two representative bursting patterns which are identified respectively to be “Fold/Big Homoclinic” bursting and “Circle/Fold cycle” bursting with bifurcations are studied. For both burstings, excitatory modulation can induce less spikes per burst for suitable time delay and strength of the self-feedback/autapse, because the modulation can change the initial or termination phases of the burst. For the former bursting composed of quiescent state and burst, the mean firing frequency exhibits increase, due to that the quiescent state becomes much shorter than the burst. However, for the latter bursting pattern with more complex behavior which is depolarization block lying between burst and quiescent state, the firing frequency manifests decrease in a wide range of time delay and strength, because the duration of both depolarization block and quiescent state becomes long. Therefore, the decrease degree of spike number per burst is larger than that of the bursting period, which is the cause for the decrease of firing frequency. Such reduced bursting activity is explained with the relations between the bifurcation points of the fast subsystem and the bursting trajectory. The present paper provides novel examples of paradoxical phenomenon that the excitatory effect induces negative responses, which presents possible novel modulation measures and potential functions of excitatory self-feedback/autapse to reduce bursting activities.

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“…Anatomical observations suggest that autapses might be used as compensatory replacements for injured axons [59], or to enhance persistent neural activity (that is supposed to be) elementary for short-term memory storage [60]. Some research papers have shown that autapses can significantly influence the dynamics of single-neurons [61] and neural networks, including synchronization [62,63], SR [64], CR [54,65], and ISR [66]. However, no study have investigated the effects of autapses on SISR and how these effects can be combined with the network topology to enhance the coherence of oscillations induced by SISR -this is one objective of the current paper.…”

Section: Introductionmentioning

confidence: 99%

Control of noise-induced coherent oscillations in three-neuron motifs

Bönsel¹,

Krauß²,

Metzner³

et al. 2021

Preprint

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The phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we investigate SISR and its control in neural network motifs made up of Morris-Lecar neurons. First, for single neurons, we show that changes in electrical autaptic parameters do not affect the degree of the coherence of the oscillations due to SISR, but rather reduces the interval of the noise intensity in which the degree is high. While changes in chemical autaptic parameters do affect both the degree of SISR and the interval of noise intensity in which the degree of SISR is high. Secondly, for single motifs, we show that the degree of SISR and the interval of the noise intensity in which this degree is high depends on the nature (electrical or chemical) of the synaptic time-delayed couplings between the neurons and the topology of the motif: (i) for all topologies of electrical motifs, the weaker couplings and the shorter the time delays between the neurons, the higher the degree of SISR and the larger the noise interval in which this degree is high; (ii) for most topologies of chemical motifs, variations in the synap-

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Coherence resonance in an autaptic Hodgkin–Huxley neuron with time delay

Cited by 37 publications

References 55 publications

Chaotic resonance in Izhikevich neural network motifs under electromagnetic induction

Chaotic resonance in Izhikevich neural network motifs under electromagnetic induction

Delayed excitatory self-feedback-induced negative responses of complex neuronal bursting patterns*

Control of noise-induced coherent oscillations in three-neuron motifs

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