000878012 001__ 878012
000878012 005__ 20240313103127.0
000878012 0247_ $$2Handle$$a2128/26352
000878012 037__ $$aFZJ-2020-02581
000878012 1001_ $$0P:(DE-Juel1)171384$$aKeup, Christian$$b0$$eCorresponding author
000878012 1112_ $$aCOSYNE$$cDENVER$$d2020-02-27 - 2020-03-03$$wUSA
000878012 245__ $$aTransient chaotic dimensionality expansion by recurrent networks
000878012 260__ $$c2020
000878012 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1607094365_15664
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000878012 520__ $$aCortical 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.
000878012 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000878012 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
000878012 536__ $$0G:(DE-82)EXS-SF-neuroIC002$$aneuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002)$$cEXS-SF-neuroIC002$$x2
000878012 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x3
000878012 7001_ $$0P:(DE-HGF)0$$aTobias, Kühn$$b1
000878012 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b2
000878012 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b3
000878012 8564_ $$uhttps://juser.fz-juelich.de/record/878012/files/Extended%20Abstract.pdf$$yOpenAccess
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000878012 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171384$$aForschungszentrum Jülich$$b0$$kFZJ
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000878012 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b3$$kFZJ
000878012 9131_ $$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$$x0
000878012 9141_ $$y2020
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000878012 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000878012 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000878012 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
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000878012 980__ $$aI:(DE-Juel1)INM-10-20170113
000878012 980__ $$aI:(DE-Juel1)IAS-6-20130828
000878012 981__ $$aI:(DE-Juel1)IAS-6-20130828