TY - GEN
T1 - The step size impact on the computational cost of spiking neuron simulation
AU - Valadez-Godinez, Sergio
AU - Sossa, Humberto
AU - Santiago-Montero, Raul
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/8
Y1 - 2018/1/8
N2 - 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 - 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.
KW - Cerebral cortex
KW - Differential equation
KW - Power-law distribution
KW - Runge-Kutta
KW - Simulation
KW - Spiking neuron
KW - Time step
UR - http://www.scopus.com/inward/record.url?scp=85040324293&partnerID=8YFLogxK
U2 - 10.1109/SAI.2017.8252176
DO - 10.1109/SAI.2017.8252176
M3 - Contribución a la conferencia
AN - SCOPUS:85040324293
T3 - Proceedings of Computing Conference 2017
SP - 722
EP - 728
BT - Proceedings of Computing Conference 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 SAI Computing Conference 2017
Y2 - 18 July 2017 through 20 July 2017
ER -