Title :
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
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.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2294016
