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@ARTICLE{Heiberg:851430,
author = {Heiberg, Thomas and Kriener, Birgit and Tetzlaff, Tom and
Einevoll, Gaute T. and Plesser, Hans E.},
title = {{F}iring-rate models for neurons with a broad repertoire of
spiking behaviors},
journal = {Journal of computational neuroscience},
volume = {45},
number = {2},
issn = {0929-5313},
address = {Dordrecht [u.a.]},
publisher = {Springer Science + Business Media B.V},
reportid = {FZJ-2018-05077},
pages = {103-132},
year = {2018},
abstract = {Capturing the response behavior of spiking neuron models
with rate-based models facilitates the investigation of
neuronal networks using powerful methods for rate-based
network dynamics. To this end, we investigate the responses
of two widely used neuron model types, the Izhikevich and
augmented multi-adapative threshold (AMAT) models, to a
range of spiking inputs ranging from step responses to
natural spike data. We find (i) that linear-nonlinear firing
rate models fitted to test data can be used to describe the
firing-rate responses of AMAT and Izhikevich spiking neuron
models in many cases; (ii) that firing-rate responses are
generally too complex to be captured by first-order low-pass
filters but require bandpass filters instead; (iii) that
linear-nonlinear models capture the response of AMAT models
better than of Izhikevich models; (iv) that the wide range
of response types evoked by current-injection experiments
collapses to few response types when neurons are driven by
stationary or sinusoidally modulated Poisson input; and (v)
that AMAT and Izhikevich models show different responses to
spike input despite identical responses to current
injections. Together, these findings suggest that rate-based
models of network dynamics may capture a wider range of
neuronal response properties by incorporating second-order
bandpass filters fitted to responses of spiking model
neurons. These models may contribute to bringing rate-based
network modeling closer to the reality of biological
neuronal networks.},
cin = {INM-6 / IAS-6 / INM-10},
ddc = {610},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
I:(DE-Juel1)INM-10-20170113},
pnm = {574 - Theory, modelling and simulation (POF3-574) / HBP
SGA2 - Human Brain Project Specific Grant Agreement 2
(785907) / HBP SGA1 - Human Brain Project Specific Grant
Agreement 1 (720270) / SMHB - Supercomputing and Modelling
for the Human Brain (HGF-SMHB-2013-2017) / BRAINSCALES -
Brain-inspired multiscale computation in neuromorphic hybrid
systems (269921)},
pid = {G:(DE-HGF)POF3-574 / G:(EU-Grant)785907 /
G:(EU-Grant)720270 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
G:(EU-Grant)269921},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:30146661},
UT = {WOS:000447740300003},
doi = {10.1007/s10827-018-0693-9},
url = {https://juser.fz-juelich.de/record/851430},
}