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

Alireza Bahramian; Janarthanan Ramadoss; Fahimeh Nazarimehr; Karthikeyan Rajagopal; Sajad Jafari; Iqtadar Hussain

doi:10.1016/j.jtbi.2022.111062

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

J Theor Biol. 2022 Apr 21:539:111062. doi: 10.1016/j.jtbi.2022.111062. Epub 2022 Feb 12.

Authors

Alireza Bahramian¹, Janarthanan Ramadoss², Fahimeh Nazarimehr¹, Karthikeyan Rajagopal³, Sajad Jafari⁴, Iqtadar Hussain⁵

Affiliations

¹ Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran.
² Centre for Artificial Intelligence, Chennai Institute of Technology, India.
³ Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India.
⁴ Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran; Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Iran.
⁵ Mathematics Program, Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, 2713 Doha, Qatar. Electronic address: Iqtadarqau@qu.edu.qa.

PMID: 35167840
DOI: 10.1016/j.jtbi.2022.111062

Abstract

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.

Keywords: Complexity; Firing rate; Map; Neuron model; Spike.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Action Potentials / physiology
Humans
Models, Neurological*
Neurons* / physiology