On the capabilities and computational costs of neuron models

Michael J. Skocik; Lyle N. Long

doi:10.1109/TNNLS.2013.2294016

On the capabilities and computational costs of neuron models

Michael J. Skocik, Lyle N. Long

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

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.

Original language	English (US)
Article number	6687241
Pages (from-to)	1474-1483
Number of pages	10
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	25
Issue number	8
DOIs	https://doi.org/10.1109/TNNLS.2013.2294016
State	Published - Aug 2014

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/TNNLS.2013.2294016

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On the capabilities and computational costs of neuron models

Abstract

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