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Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants

Michiel D'Haene UGent, Benjamin Schrauwen UGent, Jan Van Campenhout UGent and Dirk Stroobandt UGent (2009) NEURAL COMPUTATION. 21(4). p.1068-1099
abstract
The simulation of spiking neural networks (SNNs) is known to be a very time-consuming task. This limits the size of SNN that can be simulated in reasonable time or forces users to overly limit the complexity of the neuron models. This is one of the driving forces behind much of the recent research on event-driven simulation strategies. Although event-driven simulation allows precise and efficient simulation of certain spiking neuron models, it is not straightforward to generalize the technique to more complex neuron models, mostly because the firing time of these neuron models is computationally expensive to evaluate. Most solutions proposed in literature concentrate on algorithms that can solve this problem efficiently. However, these solutions do not scale well when more state variables are involved in the neuron model, which is, for example, the case when multiple synaptic time constants for each neuron are used. In this letter, we show that an exact prediction of the firing time is not required in order to guarantee exact simulation results. Several techniques are presented that try to do the least possible amount of work to predict the firing times. We propose an elegant algorithm for the simulation of leaky integrate-and-fire (LIF) neurons with an arbitrary number of (unconstrained) synaptic time constants, which is able to combine these algorithmic techniques efficiently, resulting in very high simulation speed. Moreover, our algorithm is highly independent of the complexity (i. e., number of synaptic time constants) of the underlying neuron model.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
RECOGNITION, INTEGRATION, STATE, NETWORK SIMULATIONS, FIRE MODELS, PLASTICITY, EFFICIENT, SYNAPSES
journal title
NEURAL COMPUTATION
Neural Comput.
volume
21
issue
4
pages
1068 - 1099
Web of Science type
Article
Web of Science id
000264896200007
JCR category
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
JCR impact factor
2.175 (2009)
JCR rank
28/102 (2009)
JCR quartile
2 (2009)
ISSN
0899-7667
DOI
10.1162/neco.2008.02-08-707
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
906092
handle
http://hdl.handle.net/1854/LU-906092
date created
2010-03-16 14:31:57
date last changed
2010-04-12 09:35:48
@article{906092,
  abstract     = {The simulation of spiking neural networks (SNNs) is known to be a very time-consuming task. This limits the size of SNN that can be simulated in reasonable time or forces users to overly limit the complexity of the neuron models. This is one of the driving forces behind much of the recent research on event-driven simulation strategies.
Although event-driven simulation allows precise and efficient simulation of certain spiking neuron models, it is not straightforward to generalize the technique to more complex neuron models, mostly because the firing time of these neuron models is computationally expensive to evaluate. Most solutions proposed in literature concentrate on algorithms that can solve this problem efficiently. However, these solutions do not scale well when more state variables are involved in the neuron model, which is, for example, the case when multiple synaptic time constants for each neuron are used.
In this letter, we show that an exact prediction of the firing time is not required in order to guarantee exact simulation results. Several techniques are presented that try to do the least possible amount of work to predict the firing times. We propose an elegant algorithm for the simulation of leaky integrate-and-fire (LIF) neurons with an arbitrary number of (unconstrained) synaptic time constants, which is able to combine these algorithmic techniques efficiently, resulting in very high simulation speed. Moreover, our algorithm is highly independent of the complexity (i. e., number of synaptic time constants) of the underlying neuron model.},
  author       = {D'Haene, Michiel and Schrauwen, Benjamin and Van Campenhout, Jan and Stroobandt, Dirk},
  issn         = {0899-7667},
  journal      = {NEURAL COMPUTATION},
  keyword      = {RECOGNITION,INTEGRATION,STATE,NETWORK SIMULATIONS,FIRE MODELS,PLASTICITY,EFFICIENT,SYNAPSES},
  language     = {eng},
  number       = {4},
  pages        = {1068--1099},
  title        = {Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants},
  url          = {http://dx.doi.org/10.1162/neco.2008.02-08-707},
  volume       = {21},
  year         = {2009},
}

Chicago
D’Haene, Michiel, Benjamin Schrauwen, Jan Van Campenhout, and Dirk Stroobandt. 2009. “Accelerating Event-driven Simulation of Spiking Neurons with Multiple Synaptic Time Constants.” Neural Computation 21 (4): 1068–1099.
APA
D’Haene, M., Schrauwen, B., Van Campenhout, J., & Stroobandt, D. (2009). Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants. NEURAL COMPUTATION, 21(4), 1068–1099.
Vancouver
1.
D’Haene M, Schrauwen B, Van Campenhout J, Stroobandt D. Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants. NEURAL COMPUTATION. 2009;21(4):1068–99.
MLA
D’Haene, Michiel, Benjamin Schrauwen, Jan Van Campenhout, et al. “Accelerating Event-driven Simulation of Spiking Neurons with Multiple Synaptic Time Constants.” NEURAL COMPUTATION 21.4 (2009): 1068–1099. Print.