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001     844705
005     20240313094846.0
037 _ _ |a FZJ-2018-02087
100 1 _ |a Tetzlaff, Tom
|0 P:(DE-Juel1)145211
|b 0
|e Corresponding author
111 2 _ |a Beyond Digital Computing 2018
|c Heidelberg
|d 2018-03-19 - 2018-03-21
|w Germany
245 _ _ |a Deterministic networks for probabilistic computing
260 _ _ |c 2018
336 7 _ |a Abstract
|b abstract
|m abstract
|0 PUB:(DE-HGF)1
|s 1531480086_3397
|2 PUB:(DE-HGF)
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Abstract
|2 DataCite
336 7 _ |a OTHER
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520 _ _ |a 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.
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 0
536 _ _ |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)
|0 G:(DE-Juel1)HGF-SMHB-2013-2017
|c HGF-SMHB-2013-2017
|f SMHB
|x 1
536 _ _ |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)
|0 G:(EU-Grant)720270
|c 720270
|f H2020-Adhoc-2014-20
|x 2
700 1 _ |a Jordan, Jakob
|0 P:(DE-Juel1)151356
|b 1
700 1 _ |a Petrovici, Mihai A.
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Breitwieser, Oliver
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Schemmle, Johannes
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Meier, Karlheinz
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Diesmann, Markus
|0 P:(DE-Juel1)144174
|b 6
|u fzj
909 C O |o oai:juser.fz-juelich.de:844705
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
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|v Theory, modelling and simulation
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914 1 _ |y 2018
920 _ _ |l no
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
|k INM-6
|l Computational and Systems Neuroscience
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
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|l Theoretical Neuroscience
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920 1 _ |0 I:(DE-82)080009_20140620
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920 1 _ |0 I:(DE-Juel1)INM-10-20170113
|k INM-10
|l Jara-Institut Brain structure-function relationships
|x 3
980 _ _ |a abstract
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a I:(DE-82)080009_20140620
980 _ _ |a I:(DE-Juel1)INM-10-20170113
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981 _ _ |a I:(DE-Juel1)IAS-6-20130828

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