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000255703 037__ $$aFZJ-2015-05833
000255703 1001_ $$0P:(DE-Juel1)151356$$aJordan, Jakob$$b0$$eCorresponding author
000255703 1112_ $$a11th Göttingen Meeting of the German Neuroscience Society$$cGöttingen$$d2015-03-18 - 2015-03-21$$wGermany
000255703 245__ $$aThe effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study
000255703 260__ $$c2015
000255703 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1443615230_3784
000255703 3367_ $$033$$2EndNote$$aConference Paper
000255703 3367_ $$2DataCite$$aOutput Types/Conference Abstract
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000255703 3367_ $$2BibTeX$$aINPROCEEDINGS
000255703 520__ $$aCorrelations in neural activity can severely impair the processing of information in neural networks.In finite-size networks, correlations are however inevitable due to common presynaptic sources.Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks,can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. [1,2]For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics.Theory, however, suggests that the effect is a general phenomenon, present in any system with inhibitory feedback,irrespective of the details of the network structure and the neuron and synapse properties.Here, we investigate the effect of network heterogeneity on correlations in sparse random networks of inhibitory neurons with conductance-based synapses.Accelerated neuromorphic hardware [3] is used as a user-friendly stand-alone research tool to emulate these networks.The configurability of the hardware substrate enables us to modulate the extent of network heterogeneity in a systematic manner.We selectively study the effects of shared-input (light gray symbols in Fig.) and recurrent connections (black and dark gray symbols in Fig.) on correlations in synaptic inputs (Fig a) and spike trains (Fig b).Our results confirm that shared-input correlations are actively suppressed by inhibitory feedback also in highly heterogeneous networks exhibiting broad, heavy-tailed firing-rate distributions.However, while cell and synapse heterogeneities lead to a reduction of shared-input correlations (feedforward decorrelation),feedback decorrelation is impaired as a consequence of diminished effective feedback (see Fig.).Acknowledgments: Partially supported by the Helmholtz Association portfolio theme SMHB, the Jülich Aachen Research Alliance (JARA), EU Grant 269921 (BrainScaleS), and EU Grant 604102 (Human Brain Project, HBP).[1] Renart et al. (2010), Science 327:587–590[2] Tetzlaff et al. (2012), PLoS Comp Biol 8(8):e1002596[3] Pfeil et al. (2013), Front Neurosci 7:11
000255703 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000255703 536__ $$0G:(EU-Grant)269921$$aBRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921)$$c269921$$fFP7-ICT-2009-6$$x1
000255703 536__ $$0G:(EU-Grant)604102$$aHBP - The Human Brain Project (604102)$$c604102$$fFP7-ICT-2013-FET-F$$x2
000255703 536__ $$0G:(DE-Juel1)HGF-SystemsBiology$$aHASB - Helmholtz Alliance on Systems Biology (HGF-SystemsBiology)$$cHGF-SystemsBiology$$fHASB-2008-2012$$x3
000255703 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x4
000255703 7001_ $$0P:(DE-HGF)0$$aPfeil, Thomas$$b1
000255703 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b2
000255703 7001_ $$0P:(DE-HGF)0$$aGrübl, Andreas$$b3
000255703 7001_ $$0P:(DE-HGF)0$$aSchemmel, Johannes$$b4
000255703 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b5
000255703 7001_ $$0P:(DE-HGF)0$$aMeier, Karlheinz$$b6
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000255703 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151356$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000255703 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich GmbH$$b2$$kFZJ
000255703 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich GmbH$$b5$$kFZJ
000255703 9131_ $$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$$x0
000255703 9141_ $$y2015
000255703 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000255703 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000255703 980__ $$aabstract
000255703 980__ $$aVDB
000255703 980__ $$aI:(DE-Juel1)INM-6-20090406
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000255703 981__ $$aI:(DE-Juel1)IAS-6-20130828