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| 001 | 878012 | ||
| 005 | 20240313103127.0 | ||
| 024 | 7 | _ | |a 2128/26352 |2 Handle |
| 037 | _ | _ | |a FZJ-2020-02581 |
| 100 | 1 | _ | |a Keup, Christian |0 P:(DE-Juel1)171384 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a COSYNE |c DENVER |d 2020-02-27 - 2020-03-03 |w USA |
| 245 | _ | _ | |a Transient chaotic dimensionality expansion by recurrent networks |
| 260 | _ | _ | |c 2020 |
| 336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1607094365_15664 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 520 | _ | _ | |a Cortical neurons communicate with spikes, which are discrete events in time. Functional network models often employ rate units that are continuously coupled by analog signals. Is there a benefit of discrete signaling? By a unified mean-field theory for large random networks of rate and binary units, we show that both models have identical second order statistics. Their stimulus processing properties, however, are radically different: We discover a chaotic submanifold in binary networks that does not exist in rate models. Its dimensionality increases with time after stimulus onset and reaches a fixed point that depends on the synaptic coupling strength. Low dimensional stimuli are transiently expanded into higher-dimensional representations that live within the manifold. We find that classification performance peaks when stimulus dimensionality matches the submanifold dimension; typically within a single neuronal time constant. Classification shows a high resilience to noise that exceeds rate models by orders of magnitude. Our theory mechanistically explains all these observations.These findings have several implications. 1) Optimal performance is reached with weaker synapses in discrete state networks compared to rate models; implying lower energetic costs for synaptic transmission. 2) The classification mechanism is robust to noise, compatible with fluctuations in biophysical systems. 3) Optimal performance is reached when each neuron in the network has been activated only once; this demonstrates efficient event-based computation with short latencies. 4) The presence of a chaotic sub-manifold has implications for the variability of neuronal activity; the theory predicts a transient increase of variability after stimulus onset. Our theory thus provides a new link between recurrent and chaotic dynamics of functional networks, neuronal variability, and dimensionality of neuronal responses. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 536 | _ | _ | |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |x 1 |c PHD-NO-GRANT-20170405 |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |
| 536 | _ | _ | |0 G:(DE-82)EXS-SF-neuroIC002 |x 2 |c EXS-SF-neuroIC002 |a neuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002) |
| 536 | _ | _ | |a MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018) |0 G:(DE-Juel1)HGF-SMHB-2014-2018 |c HGF-SMHB-2014-2018 |f MSNN |x 3 |
| 700 | 1 | _ | |a Tobias, Kühn |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Dahmen, David |0 P:(DE-Juel1)156459 |b 2 |
| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 3 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/878012/files/Extended%20Abstract.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/878012/files/Extended%20Abstract.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:878012 |p openaire |p open_access |p VDB |p driver |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171384 |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)144806 |
| 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 2020 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 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)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a abstract |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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