The quantitative single-neuron modeling competition. Jolivet, R., Schürmann, F., Berger, T. K, Naud, R., Gerstner, W., & Roth, A. Biol Cybern, 99(4-5):417--426, Nov, 2008. abstract bibtex As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshold.
@article{ Jolivet_etal08b,
author = {Renaud Jolivet and Felix Schürmann and Thomas K Berger and Richard
Naud and Wulfram Gerstner and Arnd Roth},
title = {The quantitative single-neuron modeling competition.},
journal = {Biol Cybern},
year = {2008},
volume = {99},
pages = {417--426},
number = {4-5},
month = {Nov},
abstract = {As large-scale, detailed network modeling projects are flourishing
in the field of computational neuroscience, it is more and more important
to design single neuron models that not only capture qualitative
features of real neurons but are quantitatively accurate in silico
representations of those. Recent years have seen substantial effort
being put in the development of algorithms for the systematic evaluation
and optimization of neuron models with respect to electrophysiological
data. It is however difficult to compare these methods because of
the lack of appropriate benchmark tests. Here, we describe one such
effort of providing the community with a standardized set of tests
to quantify the performances of single neuron models. Our effort
takes the form of a yearly challenge similar to the ones which have
been present in the machine learning community for some time. This
paper gives an account of the first two challenges which took place
in 2007 and 2008 and discusses future directions. The results of
the competition suggest that best performance on data obtained from
single or double electrode current or conductance injection is achieved
by models that combine features of standard leaky integrate-and-fire
models with a second variable reflecting adaptation, refractoriness,
or a dynamic threshold.},
keywords = {Algorithms; Models, Neurological; Neurology, trends; Neurons, physiology}
}
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