### Abstract

Original language | American English |
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Pages | 722-728 |

Number of pages | 649 |

DOIs | |

State | Published - 8 Jan 2018 |

Event | Proceedings of Computing Conference 2017 - Duration: 8 Jan 2018 → … |

### Conference

Conference | Proceedings of Computing Conference 2017 |
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Period | 8/01/18 → … |

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### Cite this

*The step size impact on the computational cost of spiking neuron simulation*. 722-728. Paper presented at Proceedings of Computing Conference 2017, . https://doi.org/10.1109/SAI.2017.8252176

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**The step size impact on the computational cost of spiking neuron simulation.** / Valadez-Godinez, Sergio; Sossa, Humberto; Santiago-Montero, Raul.

Research output: Contribution to conference › Paper

TY - CONF

T1 - The step size impact on the computational cost of spiking neuron simulation

AU - Valadez-Godinez, Sergio

AU - Sossa, Humberto

AU - Santiago-Montero, Raul

PY - 2018/1/8

Y1 - 2018/1/8

N2 - © 2017 IEEE. Spiking neurons are mathematical models that simulate the generation of the electrical pulse at the neuron membrane. Most spiking neurons are expressed as a non-linear system of ordinary differential equations. Because these systems are hard to solve analytically, they must be solved using a numerical method through a discrete sequence of time steps. The step length is a factor affecting both the accuracy and computational cost of spiking neuron simulation. It is known the step size implications on the accuracy for some spiking neurons. However, it is unknown in which way the step size impacts the computational cost. We found that the computational cost as a function of the step length follows a power-law distribution. We reviewed the Leaky Integrate-and-Fire, Izhikevich, and Hodgkin-Huxley spiking neurons. Additionally, it was found that, with any step size, simulating the cerebral cortex in a sequential processing computer is prohibitive.

AB - © 2017 IEEE. Spiking neurons are mathematical models that simulate the generation of the electrical pulse at the neuron membrane. Most spiking neurons are expressed as a non-linear system of ordinary differential equations. Because these systems are hard to solve analytically, they must be solved using a numerical method through a discrete sequence of time steps. The step length is a factor affecting both the accuracy and computational cost of spiking neuron simulation. It is known the step size implications on the accuracy for some spiking neurons. However, it is unknown in which way the step size impacts the computational cost. We found that the computational cost as a function of the step length follows a power-law distribution. We reviewed the Leaky Integrate-and-Fire, Izhikevich, and Hodgkin-Huxley spiking neurons. Additionally, it was found that, with any step size, simulating the cerebral cortex in a sequential processing computer is prohibitive.

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U2 - 10.1109/SAI.2017.8252176

DO - 10.1109/SAI.2017.8252176

M3 - Paper

SP - 722

EP - 728

ER -