000844705 001__ 844705
000844705 005__ 20240313094846.0
000844705 037__ $$aFZJ-2018-02087
000844705 1001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b0$$eCorresponding author
000844705 1112_ $$aBeyond Digital Computing 2018$$cHeidelberg$$d2018-03-19 - 2018-03-21$$wGermany
000844705 245__ $$aDeterministic networks for probabilistic computing
000844705 260__ $$c2018
000844705 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1531480086_3397
000844705 3367_ $$033$$2EndNote$$aConference Paper
000844705 3367_ $$2BibTeX$$aINPROCEEDINGS
000844705 3367_ $$2DRIVER$$aconferenceObject
000844705 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000844705 3367_ $$2ORCID$$aOTHER
000844705 520__ $$aNeuronal-network models of high-level brain function often rely on the presence of stochasticity. The majority of these models assumes that each neuron is equipped with its own private source of randomness, often in the form of uncorrelated external noise. In biological neuronal networks, the origin of this noise remains unclear. In hardware implementations, the number of noise sources is limited due to space and bandwidth constraints. Hence, neurons in large networks have to share noise sources. We show that the resulting shared-noise correlations can significantly impair the computational performance of stochastic neuronal networks, but that this problem is naturally overcome by generating noise with deterministic recurrent neuronal networks. By virtue of the decorrelating effect of inhibitory feedback, a network of a few hundred neurons can serve as a natural source of uncorrelated noise for large ensembles of functional networks, each comprising thousands of units.
000844705 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000844705 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x1
000844705 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x2
000844705 7001_ $$0P:(DE-Juel1)151356$$aJordan, Jakob$$b1
000844705 7001_ $$0P:(DE-HGF)0$$aPetrovici, Mihai A.$$b2
000844705 7001_ $$0P:(DE-HGF)0$$aBreitwieser, Oliver$$b3
000844705 7001_ $$0P:(DE-HGF)0$$aSchemmle, Johannes$$b4
000844705 7001_ $$0P:(DE-HGF)0$$aMeier, Karlheinz$$b5
000844705 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b6$$ufzj
000844705 909CO $$ooai:juser.fz-juelich.de:844705$$pec_fundedresources$$pVDB$$popenaire
000844705 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich$$b0$$kFZJ
000844705 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151356$$aForschungszentrum Jülich$$b1$$kFZJ
000844705 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b6$$kFZJ
000844705 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000844705 9141_ $$y2018
000844705 920__ $$lno
000844705 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000844705 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000844705 9201_ $$0I:(DE-82)080009_20140620$$kJARA-FIT$$lJARA-FIT$$x2
000844705 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x3
000844705 980__ $$aabstract
000844705 980__ $$aVDB
000844705 980__ $$aI:(DE-Juel1)INM-6-20090406
000844705 980__ $$aI:(DE-Juel1)IAS-6-20130828
000844705 980__ $$aI:(DE-82)080009_20140620
000844705 980__ $$aI:(DE-Juel1)INM-10-20170113
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000844705 981__ $$aI:(DE-Juel1)IAS-6-20130828