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CLC number: TN710; O59

On-line Access: 2020-09-09

Received: 2019-11-09

Revision Accepted: 2020-01-25

Crosschecked: 2020-03-31

Cited: 0

Clicked: 452

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jun Ma

https://orcid.org/0000-0002-6127-000X

Yong Liu

https://orcid.org/0000-0002-9387-5417

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.9 P.1387-1396

http://doi.org/10.1631/FITEE.1900606


A new photosensitive neuron model and its dynamics


Author(s):  Yong Liu, Wan-jiang Xu, Jun Ma, Faris Alzahrani, Aatef Hobiny

Affiliation(s):  School of Mathematics and Statistics, Yancheng Teachers University, Yancheng 224002, China; more

Corresponding email(s):   hyperchaos@163.com, hyperchaos@lut.edu.cn

Key Words:  Photosensitive neuron, Neuron model, Bifurcation, Bursting, Photocell


Yong Liu, Wan-jiang Xu, Jun Ma, Faris Alzahrani, Aatef Hobiny. A new photosensitive neuron model and its dynamics[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1387-1396.

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Abstract: 
Biological neurons can receive inputs and capture a variety of external stimuli, which can be encoded and transmitted as different electric signals. Thus, the membrane potential is adjusted to activate the appropriate firing modes. Indeed, reliable neuron models should take intrinsic biophysical effects and functional encoding into consideration. One fascinating and important question is the physical mechanism for the transcription of external signals. External signals can be transmitted as a transmembrane current or a signal voltage for generating action potentials. We present a photosensitive neuron model to estimate the nonlinear encoding and responses of neurons driven by external optical signals. In the model, a photocell (phototube) is used to activate a simple FitzHugh-Nagumo (FHN) neuron, and then external optical signals (illumination) are imposed to excite the photocell for generating a time-varying current/voltage source. The photocell-coupled FHN neuron can therefore capture and encode external optical signals, similar to artificial eyes. We also present detailed bifurcation analysis for estimating the mode transition and firing pattern selection of neuronal electrical activities. The sampled time series can reproduce the main characteristics of biological neurons (quiescent, spiking, bursting, and even chaotic behaviors) by activating the photocell in the neural circuit. These results could be helpful in giving possible guidance for studying neurodynamics and applying neural circuits to detect optical signals.

一类新的光电神经元模型及其动力学

刘勇1,徐万江1,马军2,3,Faris ALZAHRANI4,Aatef HOBINY4
1盐城师范学院数学与统计学院,中国盐城市,224002
2兰州理工大学物理系,中国兰州市,730050
3重庆邮电大学理学院,中国重庆市,430065
4阿卜杜勒阿齐兹国王大学数学系NAAM研究组,沙特阿拉伯吉达,21589

摘要:生物神经元可感知多种外界刺激信号,这些信号可被转化为等效的电流来驱动神经元。因此,神经元的膜电位可通过外刺激调控呈现各类放电模式。实际上,可靠的神经元模型应考虑内在的生物物理效应以及功能性编码。一个重要且有趣的问题是弄清外界信号转录过程的物理机制。外界信号通常被转化为等效的跨膜电流或信号源以诱发动作电位。提出一个光电神经元模型以表达其非线性编码过程和外界光信号驱动神经元的电活动响应。在该模型中,使用一个光电管激活一个简单的FitzHugh-Nagumo(FHN)神经元电路,并施加外界光信号(光照)于光电管产生时变电流源或电压源以驱动神经元电路。这种光电管耦合的神经元电路能探测和感知外界光信号,其作用类似于人工电子眼。通过分岔详细分析神经元模态迁移和放电斑图特征。通过调制神经元电路的光电流,神经元膜电位序列可呈现静息态、尖峰放电、簇放电和混沌特征。这些结果可为进一步研究神经动力学和神经电路提供参考。

关键词:光电神经元;神经元模型;分岔;簇放电;光电管

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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