This title appears in the Scientific Report :
2015
The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study
The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study
Correlations 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,c...
Saved in:
Personal Name(s): | Jordan, Jakob (Corresponding author) |
---|---|
Pfeil, Thomas / Tetzlaff, Tom / Grübl, Andreas / Schemmel, Johannes / Diesmann, Markus / Meier, Karlheinz | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Computational and Systems Neuroscience; IAS-6 |
Imprint: |
2015
|
Conference: | 11th Göttingen Meeting of the German Neuroscience Society, Göttingen (Germany), 2015-03-18 - 2015-03-21 |
Document Type: |
Abstract |
Research Program: |
Supercomputing and Modelling for the Human Brain Helmholtz Alliance on Systems Biology The Human Brain Project Brain-inspired multiscale computation in neuromorphic hybrid systems Theory, modelling and simulation |
Publikationsportal JuSER |
LEADER | 05312nam a2200589 a 4500 | ||
---|---|---|---|
001 | 255703 | ||
005 | 20240313094859.0 | ||
037 | |a FZJ-2015-05833 | ||
100 | 1 | |a Jordan, Jakob |0 P:(DE-Juel1)151356 |b 0 |e Corresponding author | |
111 | 2 | |a 11th Göttingen Meeting of the German Neuroscience Society |c Göttingen |d 2015-03-18 - 2015-03-21 |w Germany | |
245 | |a The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study | ||
260 | |c 2015 | ||
520 | |a Correlations 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 | ||
700 | 1 | |a Pfeil, Thomas |0 P:(DE-HGF)0 |b 1 | |
700 | 1 | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 2 | |
700 | 1 | |a Grübl, Andreas |0 P:(DE-HGF)0 |b 3 | |
700 | 1 | |a Schemmel, Johannes |0 P:(DE-HGF)0 |b 4 | |
700 | 1 | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 5 | |
700 | 1 | |a Meier, Karlheinz |0 P:(DE-HGF)0 |b 6 | |
909 | C | O | |o oai:juser.fz-juelich.de:255703 |p VDB |p ec_fundedresources |p openaire |
910 | 1 | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)151356 | |
910 | 1 | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)145211 | |
910 | 1 | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)144174 | |
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 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 | |
914 | 1 | |y 2015 | |
980 | |a abstract | ||
980 | |a VDB | ||
980 | |a I:(DE-Juel1)INM-6-20090406 | ||
980 | |a I:(DE-Juel1)IAS-6-20130828 | ||
980 | |a UNRESTRICTED | ||
536 | |a Supercomputing and Modelling for the Human Brain |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |f SMHB |x 4 | ||
536 | |a Helmholtz Alliance on Systems Biology |0 G:(DE-Juel1)HGF-SystemsBiology |c HGF-SystemsBiology |f HASB-2008-2012 |x 3 | ||
536 | |a The Human Brain Project |0 G:(EU-Grant)604102 |c 604102 |f FP7-ICT-2013-FET-F |x 2 | ||
536 | |a Brain-inspired multiscale computation in neuromorphic hybrid systems |0 G:(EU-Grant)269921 |c 269921 |f FP7-ICT-2009-6 |x 1 | ||
536 | |a Theory, modelling and simulation |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 | ||
336 | |a OTHER |2 ORCID | ||
336 | |a Output Types/Conference Abstract |2 DataCite | ||
336 | |a ExWoSt-Informationen / 33 |0 33 |2 EndNote | ||
336 | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1443615230_3784 |2 PUB:(DE-HGF) | ||
336 | |a INPROCEEDINGS |2 BibTeX | ||
336 | |a conferenceObject |2 DRIVER | ||
981 | |a I:(DE-Juel1)IAS-6-20130828 | ||
920 | |k Computational and Systems Neuroscience; INM-6 |0 I:(DE-Juel1)INM-6-20090406 |l Computational and Systems Neuroscience |x 0 | ||
981 | |a I:(DE-Juel1)IAS-6-20130828 | ||
920 | |k Computational and Systems Neuroscience; IAS-6 |0 I:(DE-Juel1)IAS-6-20130828 |l Theoretical Neuroscience |x 1 | ||
990 | |a Jordan, Jakob |0 P:(DE-Juel1)151356 |b 0 |e Corresponding author | ||
991 | |a Meier, Karlheinz |0 P:(DE-HGF)0 |b 6 | ||
991 | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 5 | ||
991 | |a Schemmel, Johannes |0 P:(DE-HGF)0 |b 4 | ||
991 | |a Grübl, Andreas |0 P:(DE-HGF)0 |b 3 | ||
991 | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 2 | ||
991 | |a Pfeil, Thomas |0 P:(DE-HGF)0 |b 1 |