A simple one-dimensional map-based model of spiking neurons with wide ranges of firing rates and complexities

被引：5

作者：

Bahramian, Alireza ^{[1
]}

Ramadoss, Janarthanan ^{[2
]}

Nazarimehr, Fahimeh ^{[1
]}

Rajagopal, Karthikeyan ^{[3
]}

Jafari, Sajad ^{[1
,4
]}

Hussain, Iqtadar ^{[5
]}

机构：

[1] Amirkabir Univ Technol, Dept Biomed Engn, Tehran Polytech, Tehran, Iran

[2] Chennai Inst Technol, Ctr Artificial Intelligence, Chennai, Tamil Nadu, India

[3] Chennai Inst Technol, Ctr Nonlinear Syst, Chennai, Tamil Nadu, India

[4] Amirkabir Univ Technol, Hlth Technol Res Inst, Tehran Polytech, Tehran, Iran

[5] Qatar Univ, Coll Arts & Sci, Dept Math Stat & Phys, Math Program, Doha 2713, Qatar

来源：

JOURNAL OF THEORETICAL BIOLOGY | 2022年 / 539卷

关键词：

Neuron model; Map; Spike; Firing rate; Complexity; SINGLE-NEURON; NETWORK; DYNAMICS; NERVE; SYNCHRONIZATION; OSCILLATIONS; POPULATIONS; REDUCTION; FEATURES; ENTROPY;

D O I：

10.1016/j.jtbi.2022.111062

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper introduces a simple 1-dimensional map-based model of spiking neurons. During the past decades, dynamical models of neurons have been used to investigate the biology of human nervous systems. The models simulate experimental records of neurons' voltages using difference or differential equations. Difference neuronal models have some advantages besides the differential ones. They are usually simpler, and considering the cost of needed computations, they are more efficient. In this paper, a simple 1 dimensional map-based model of spiking neurons is introduced. Sample entropy is applied to analyze the complexity of the model's dynamics. The model can generate a wide range of time series with different firing rates and different levels of complexities. Besides, using some tools like bifurcation diagrams and cobwebs, the introduced model is analyzed.(c) 2022 Elsevier Ltd. All rights reserved.

引用

页数：8

共 74 条

[31]

HODGKIN AL, 1990, B MATH BIOL, V52, P25, DOI 10.1007/BF02459568

[32] Map-based models in neuronal dynamics [J].

Ibarz, B. ;

Casado, J. M. ;

Sanjuan, M. A. F. .

PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 2011, 501 (1-2) :1-74

[33] Bursting regimes in map-based neuron models coupled through fast threshold modulation [J].

Ibarz, Borja ;

Cao, Hongjun ;

Sanjuan, Miguel A. F. .

PHYSICAL REVIEW E, 2008, 77 (05)

[34] Classification of bursting mappings [J].

Izhikevich, EM ;

Hoppensteadt, F .

INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2004, 14 (11) :3847-3854

[35] Which model to use for cortical spiking neurons? [J].

Izhikevich, EM .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2004, 15 (05) :1063-1070

[36] Simple model of spiking neurons [J].

Izhikevich, EM .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (06) :1569-1572

[37] Sample entropy reveals an age-related reduction in the complexity of dynamic brain [J].

Jia, Yanbing ;

Gu, Huaguang ;

Luo, Qiang .

SCIENTIFIC REPORTS, 2017, 7

[38] Ion Channel Sensing: Are Fluctuations the Crux of the Matter? [J].

Kasimova, Marina A. ;

Yazici, Aysenur ;

Yudin, Yevgen ;

Granata, Daniele ;

Klein, Michael L. ;

Rohacs, Tibor ;

Carnevale, Vincenzo .

JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2018, 9 (06) :1260-1264

[39] Chimera states in a ring of map-based neurons [J].

Khaleghi, Leyla ;

Panahi, Shirin ;

Chowdhury, Sayantan Nag ;

Bogomolov, Sergey ;

Ghosh, Dibakar ;

Jafari, Sajad .

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 536

[40] The contribution of resurgent sodium current to high-frequency firing in Purkinje neurons: An experimental and modeling study [J].

Khaliq, ZM ;

Gouwens, NW ;

Raman, IM .

JOURNAL OF NEUROSCIENCE, 2003, 23 (12) :4899-4912

← 1 2 3 4 5 6 7 8 →