Dynamics and coherence resonance in a thermosensitive neuron driven by photocurrent*

Xu, Ying; Liu, Minghua; Zhu, Zhigang; Ma, Jun

doi:10.1088/1674-1056/ab9dee

Cited by 74 publications

(27 citation statements)

References 91 publications

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“…There are many available biophysical models of neuronal membrane potential dynamics (Morris and Lecar, 1981 ; Hodgkin and Huxley, 1990 ; Xu et al, 2020a , b ; Zhang et al, 2020 ). For our purpose we chose the Izhikevich model as it is quite functional and computationally effective for network simulations (Izhikevich, 2003 ):…”

Section: Methodsmentioning

confidence: 99%

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Gordleeva

Tsybina

Krivonosov

et al. 2021

Front. Cell. Neurosci.

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We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.

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Section: Methodsmentioning

confidence: 99%

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Gordleeva

Tsybina

Krivonosov

et al. 2021

Front. Cell. Neurosci.

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“…More details about the model refer to Refs. [14,30,36]. The equations of the functions are as follows:…”

Section: Modelmentioning

confidence: 99%

Fast-slow Variable Dissection with Two Slow Variables Related to Calcium Concentrations: A Case Study to Bursting in a Neural Pacemaker Model

Jia

et al. 2021

Preprint

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Neuronal bursting is an electrophysiological behavior participating in physiological or pathological functions and a complex nonlinear alternating between burst and quiescent state modulated by slow variables. Identification of dynamics of bursting modulated by two slow variables is still an open problem. In the present paper, a novel fast-slow variable dissection method with two slow variables is proposed to analyze the complex bursting in a 4-dimensional neuronal model to describe bursting associated with pathological pain. The lumenal (Clum) and intracellular (Cin) calcium concentrations are the slowest variables respectively in the quiescent state and burst duration. Questions encountered when the traditional method with one low variable is used. When Clum is taken as slow variable, the burst is successfully identified to terminate near the saddle-homoclinic bifurcation point of the fast subsystem and begin not from the saddle-node bifurcation. With Cin chosen as slow variable, Clum value of initiation point is far from the saddle-node bifurcation point, due to Clum not contained in the equation of membrane potential. To overcome this problem, both Cin and Clum are regarded as slow variables, the two-dimensional fast subsystem exhibits a saddle-node bifurcation point, which is extended to a saddle-node bifurcation curve by introducing Clum dimension. Then, the initial point of burst is successfully identified to be near the saddle-node bifurcation curve. The results present a feasible method for fast-slow variable dissection and deep understanding to the complex bursting behavior with two slow variables, which is helpful for the modulation to pathological pain.

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“…Neurons are the basic unit of the nervous system whose dynamic characteristics play an important role in the operation of the neuron system. e discharging modes of neuronal systems exhibit complex and contradictory nonlinear dynamic phenomena such as bifurcation, chaos, stochastic resonance, coherence resonance, and vibrational resonance [1][2][3][4][5][6][7]. Noise exists widely in the natural system, therefore, as a huge neural network, the existence of noise is inevitable.…”

Section: Introductionmentioning

confidence: 99%

Coherence Resonance Behavior of FitzHugh‐Nagumo Neurons Induced by Electromagnetic Field Driven by Phase Noise

Zheng

2022

Discrete Dynamics in Nature and Society

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Noise exists widely in the nervous system, and plays a crucial role in the nervous system information processing. Noise can not only enhance but also weaken the ability of the nervous system to process information. Neurons are in a complex and variable electromagnetic field. Electromagnetic induction plays an important role in regulating the changes of neuronal membrane potential. Therefore, this paper simulates the electromagnetic field environment of the nervous system with a memristor and analyses the rich coherence resonance behavior of FitzHugh-Nagumo (FHN) neuron system under the drive of phase noise. By taking the amplitude, period and noise intensity of phase noise as the main parameters and the parameters of memristor as auxiliary parameters, the two-parameter changes are made from the angle of the amplitude and period of phase noise, the amplitude and intensity of phase noise, and the noise intensity and period of phase noise, respectively. The dynamic behaviors of coherent resonance of FHN neuron system are analyzed from the amplitude and period, amplitude and intensity as well as intensity and period of phase noise, respectively. When the amplitude and period of the phase noise and the intensity and period of the phase noise are used as independent variables for the two-parameter analysis, the FHN neuron system shows rich dynamic behaviors such as coherence mono-resonance, coherence bi-resonance and coherence multi-resonance. Especially when the amplitude and period of phase noise change as two-parameter, the system presents a coherence resonance of discharge pattern with period-adding cluster discharge at the valley. When the amplitude and intensity of phase noise are taken as independent variables for two-parameter analysis, FHN neuronal system presents single or dual coherence resonance at any value of noise intensity with the change of phase noise amplitude. The simulated results show that the FHN neuron system demonstrates rich coherence resonance behaviors under the drive of phase noise when the effect of electromagnetic induction in the nervous system is simulated by memristor.

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Dynamics and coherence resonance in a thermosensitive neuron driven by photocurrent*

Cited by 74 publications

References 91 publications

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Fast-slow Variable Dissection with Two Slow Variables Related to Calcium Concentrations: A Case Study to Bursting in a Neural Pacemaker Model

Coherence Resonance Behavior of FitzHugh‐Nagumo Neurons Induced by Electromagnetic Field Driven by Phase Noise

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