On the Capabilities and Computational Costs of Neuron Models

Author

Skocik, Michael J. ; Long, Lyle N.

Author_Institution

Dept. of Phys., Pennsylvania State Univ., University Park, PA, USA

Volume

25

Issue

8

fYear

2014

fDate

Aug. 2014

Firstpage

1474

Lastpage

1483

Abstract

We review the Hodgkin-Huxley, Izhikevich, and leaky integrate-and-fire neuron models in regular spiking modes solved with the forward Euler, fourth-order Runge-Kutta, and exponential Euler methods and determine the necessary time steps and corresponding computational costs required to make the solutions accurate. We conclude that the leaky integrate-and-fire needs the least number of computations, and that the Hodgkin-Huxley and Izhikevich models are comparable in computational cost.

Keywords

Runge-Kutta methods; neural nets; Hodgkin-Huxley neuron models; Izhikevich neuron models; computational costs; exponential Euler methods; forward Euler methods; fourth-order Runge-Kutta methods; leaky integrate-and-fire neuron models; regular spiking modes; Accuracy; Biological system modeling; Biomembranes; Computational modeling; Equations; Mathematical model; Neurons; Accuracy; Hodgkin--Huxley; Hodgkin??Huxley; Izhikevich; computational costs; leaky integrate-and-fire; leaky integrate-and-fire.;

fLanguage

English

Journal_Title

Neural Networks and Learning Systems, IEEE Transactions on

Publisher

ieee

ISSN

2162-237X

Type

jour

DOI

10.1109/TNNLS.2013.2294016

Filename

6687241