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@INPROCEEDINGS{Tetzlaff:844705,
author = {Tetzlaff, Tom and Jordan, Jakob and Petrovici, Mihai A. and
Breitwieser, Oliver and Schemmle, Johannes and Meier,
Karlheinz and Diesmann, Markus},
title = {{D}eterministic networks for probabilistic computing},
reportid = {FZJ-2018-02087},
year = {2018},
abstract = {Neuronal-network models of high-level brain function often
rely on the presence of stochasticity. The majority of these
models assumes that each neuron is equipped with its own
private source of randomness, often in the form of
uncorrelated external noise. In biological neuronal
networks, the origin of this noise remains unclear. In
hardware implementations, the number of noise sources is
limited due to space and bandwidth constraints. Hence,
neurons in large networks have to share noise sources. We
show that the resulting shared-noise correlations can
significantly impair the computational performance of
stochastic neuronal networks, but that this problem is
naturally overcome by generating noise with deterministic
recurrent neuronal networks. By virtue of the decorrelating
effect of inhibitory feedback, a network of a few hundred
neurons can serve as a natural source of uncorrelated noise
for large ensembles of functional networks, each comprising
thousands of units.},
month = {Mar},
date = {2018-03-19},
organization = {Beyond Digital Computing 2018,
Heidelberg (Germany), 19 Mar 2018 - 21
Mar 2018},
cin = {INM-6 / IAS-6 / JARA-FIT / INM-10},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
$I:(DE-82)080009_20140620$ / I:(DE-Juel1)INM-10-20170113},
pnm = {574 - Theory, modelling and simulation (POF3-574) / SMHB -
Supercomputing and Modelling for the Human Brain
(HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain Project
Specific Grant Agreement 1 (720270)},
pid = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
G:(EU-Grant)720270},
typ = {PUB:(DE-HGF)1},
url = {https://juser.fz-juelich.de/record/844705},
}