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| Hauptseite > Publikationsdatenbank > Neural Networks as Sources of uncorrelated Noise for functional neural Systems > print |
| Hauptseite > Publikationsdatenbank > Neural Networks as Sources of uncorrelated Noise for functional neural Systems > print |
| 001 | 155477 | ||
| 005 | 20240313095023.0 | ||
| 037 | _ | _ | |a FZJ-2014-04643 |
| 100 | 1 | _ | |a Jordan, Jakob |0 P:(DE-Juel1)151356 |b 0 |e Corresponding Author |
| 111 | 2 | _ | |a OCCAM 2014 |c Osnabrueck |d 2014-05-07 - 2014-05-09 |w Germany |
| 245 | _ | _ | |a Neural Networks as Sources of uncorrelated Noise for functional neural Systems |
| 260 | _ | _ | |c 2014 |
| 336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1409306859_6679 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 520 | _ | _ | |a Neural-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. |
| 536 | _ | _ | |a 331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331) |0 G:(DE-HGF)POF2-331 |c POF2-331 |f POF II |x 0 |
| 536 | _ | _ | |a BRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921) |0 G:(EU-Grant)269921 |c 269921 |f FP7-ICT-2009-6 |x 1 |
| 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 2 |
| 536 | _ | _ | |a HBP - The Human Brain Project (604102) |0 G:(EU-Grant)604102 |c 604102 |f FP7-ICT-2013-FET-F |x 3 |
| 536 | _ | _ | |a 89574 - Theory, modelling and simulation (POF2-89574) |0 G:(DE-HGF)POF2-89574 |c POF2-89574 |x 4 |f POF II T |
| 700 | 1 | _ | |a Petrovici, Mihai |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Pfeil, Thomas |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Breitwieser, Oliver |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Bytschok, Ilya |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Bill, Johannes |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Gruebl, Andreas |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Schemmel, Johannes |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Meier, Karlheinz |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 9 |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 10 |
| 773 | _ | _ | |y 2014 |
| 909 | C | O | |o oai:juser.fz-juelich.de:155477 |p VDB |p ec_fundedresources |p openaire |
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| 913 | 2 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Funktion und Dysfunktion des Nervensystems |1 G:(DE-HGF)POF2-330 |0 G:(DE-HGF)POF2-331 |2 G:(DE-HGF)POF2-300 |v Signalling Pathways and Mechanisms in the Nervous System |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF2 |
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| 914 | 1 | _ | |y 2014 |
| 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 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 980 | _ | _ | |a abstract |
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