A Comparative Analysis of Numerical Methods for Solving the Leaky Integrate and Fire Neuron Model

Ghinwa El Masri, Asma Ali, Waad H. Abuwatfa, Maruf Mortula and Ghaleb A. Husseini ()
Additional contact information
Ghinwa El Masri: Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah 26666, United Arab Emirates
Asma Ali: Electrical Engineering Graduate Program, American University of Sharjah, Sharjah 26666, United Arab Emirates
Waad H. Abuwatfa: Chemical and Biological Engineering Department, American University of Sharjah, Sharjah 26666, United Arab Emirates
Maruf Mortula: Civil Engineering Department, American University of Sharjah, Sharjah 26666, United Arab Emirates
Ghaleb A. Husseini: Chemical and Biological Engineering Department, American University of Sharjah, Sharjah 26666, United Arab Emirates

Mathematics, 2023, vol. 11, issue 3, 1-15

Abstract: The human nervous system is one of the most complex systems of the human body. Understanding its behavior is crucial in drug discovery and developing medical devices. One approach to understanding such a system is to model its most basic unit, neurons. The leaky integrate and fire (LIF) method models the neurons’ response to a stimulus. Given the fact that the model’s equation is a linear ordinary differential equation, the purpose of this research is to compare which numerical analysis method gives the best results for the simplified version of this model. Adams predictor and corrector (AB4-AM4) and Heun’s methods were then used to solve the equation. In addition, this study further researches the effects of different current input models on the LIF’s voltage output. In terms of the computational time, Heun’s method was 0.01191 s on average which is much less than that of the AB-AM4 method (0.057138) for a constant DC input. As for the root mean square error, the AB-AM4 method had a much lower value (0.0061) compared to that of Heun’s method (0.3272) for the same constant input. Therefore, our results show that Heun’s method is best suited for the simplified LIF model since it had the lowest computation time of 36 ms, was stable over a larger range, and had an accuracy of 72% for the varying sinusoidal current input model.

Keywords: computational neuroscience; numerical analysis; neuroinformatics; leaky integrate and fire (LIF); Adams predictor and corrector; Heun’s method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/714/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/714/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:714-:d:1052495

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().