000155477 001__ 155477
000155477 005__ 20240313095023.0
000155477 037__ $$aFZJ-2014-04643
000155477 1001_ $$0P:(DE-Juel1)151356$$aJordan, Jakob$$b0$$eCorresponding Author
000155477 1112_ $$aOCCAM 2014$$cOsnabrueck$$d2014-05-07 - 2014-05-09$$wGermany
000155477 245__ $$aNeural Networks as Sources of uncorrelated Noise for functional neural Systems
000155477 260__ $$c2014
000155477 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1409306859_6679
000155477 3367_ $$033$$2EndNote$$aConference Paper
000155477 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000155477 3367_ $$2ORCID$$aOTHER
000155477 3367_ $$2DRIVER$$aconferenceObject
000155477 3367_ $$2BibTeX$$aINPROCEEDINGS
000155477 520__ $$aNeural-network models of brain function often rely on the presence of noise [1,2]. Yet thebiological origin of this noise remains unclear. In computer simulations and in neuromorphichardware [3,4], the number of noise sources (random-number generators) is limited. In conse-quence, neurons in large functional network models have to share noise sources and are thereforecorrelated. It is largely unclear how shared-noise correlation affect the performance of func-tional network models. Further, so far there is no solution to the problem of how a limitednumber of noise sources can supply a large number of functional units with uncorrelated noise.Here, we first demonstrate that the performance of two functional network models, attrac-tor networks [5] and neural Boltzmann machines [2], is substantially impaired by shared-noisecorrelations resulting from a limited number of noise sources. Secondly, we show that thisproblem can be overcome by replacing the finite pool of independent noise sources by a (finite)recurrent neural network. As shown recently, inhibitory feedback, abundant in biological neu-ral networks, serves as a powerful decorrelation mechanism [6,7]. Shared-noise correlations areactively suppressed by the network dynamics. By exploiting this effect, the network perfor-mance is significantly improved. Finally, we demonstrate the decorrelating effect of inhibitoryfeedback in a heterogeneous network implemented in an analog neuromorphic substrate [8]. Insummary, we show that recurrent neural networks can serve as natural finite-size noise sourcesfor functional neural networks, both in biological and in synthetic neuromorphic substrates.
000155477 536__ $$0G:(DE-HGF)POF2-331$$a331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331)$$cPOF2-331$$fPOF II$$x0
000155477 536__ $$0G:(EU-Grant)269921$$aBRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921)$$c269921$$fFP7-ICT-2009-6$$x1
000155477 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000155477 536__ $$0G:(EU-Grant)604102$$aHBP - The Human Brain Project (604102)$$c604102$$fFP7-ICT-2013-FET-F$$x3
000155477 536__ $$0G:(DE-HGF)POF2-89574$$a89574 - Theory, modelling and simulation (POF2-89574)$$cPOF2-89574$$fPOF II T$$x4
000155477 7001_ $$0P:(DE-HGF)0$$aPetrovici, Mihai$$b1
000155477 7001_ $$0P:(DE-HGF)0$$aPfeil, Thomas$$b2
000155477 7001_ $$0P:(DE-HGF)0$$aBreitwieser, Oliver$$b3
000155477 7001_ $$0P:(DE-HGF)0$$aBytschok, Ilya$$b4
000155477 7001_ $$0P:(DE-HGF)0$$aBill, Johannes$$b5
000155477 7001_ $$0P:(DE-HGF)0$$aGruebl, Andreas$$b6
000155477 7001_ $$0P:(DE-HGF)0$$aSchemmel, Johannes$$b7
000155477 7001_ $$0P:(DE-HGF)0$$aMeier, Karlheinz$$b8
000155477 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b9
000155477 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b10
000155477 773__ $$y2014
000155477 909CO $$ooai:juser.fz-juelich.de:155477$$popenaire$$pec_fundedresources$$pVDB
000155477 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151356$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000155477 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich GmbH$$b9$$kFZJ
000155477 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich GmbH$$b10$$kFZJ
000155477 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000155477 9131_ $$0G:(DE-HGF)POF2-331$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vSignalling Pathways and Mechanisms in the Nervous System$$x0
000155477 9131_ $$0G:(DE-HGF)POF2-89574$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vTheory, modelling and simulation$$x1
000155477 9141_ $$y2014
000155477 920__ $$lno
000155477 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000155477 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000155477 980__ $$aabstract
000155477 980__ $$aVDB
000155477 980__ $$aI:(DE-Juel1)INM-6-20090406
000155477 980__ $$aI:(DE-Juel1)IAS-6-20130828
000155477 980__ $$aUNRESTRICTED
000155477 981__ $$aI:(DE-Juel1)IAS-6-20130828
000155477 981__ $$aI:(DE-Juel1)IAS-6-20130828