000885628 001__ 885628
000885628 005__ 20240313103121.0
000885628 0247_ $$2Handle$$a2128/25881
000885628 037__ $$aFZJ-2020-03969
000885628 1001_ $$0P:(DE-Juel1)171384$$aKeup, Christian$$b0$$eCorresponding author$$ufzj
000885628 245__ $$aTransient chaotic dimensionality expansion by recurrent networks
000885628 260__ $$c2020
000885628 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1602682863_10885
000885628 3367_ $$2ORCID$$aWORKING_PAPER
000885628 3367_ $$028$$2EndNote$$aElectronic Article
000885628 3367_ $$2DRIVER$$apreprint
000885628 3367_ $$2BibTeX$$aARTICLE
000885628 3367_ $$2DataCite$$aOutput Types/Working Paper
000885628 520__ $$aNeurons 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 we show that large random networks of rate and binary units have identical second order statistics. Yet their stimulus processing properties are radically different: We discover a chaotic sub-manifold in binary networks that does not exist in rate models. Its dimensionality increases with time after stimulus onset and reaches a fixed point depending on the synaptic coupling strength. Low dimensional stimuli are transiently expanded into higher-dimensional representations within this manifold. High noise resilience persists not only near the edge of chaos, but throughout the chaotic regime. In rate models of spiking activity, the effective spiking noise suppresses chaos, severely impairing classification performance. Chaotic rate networks without effective spiking noise also show the transient performance boost. The transitions to chaos in the two models do not coincide and have qualitatively different causes. Our theory mechanistically explains these observations. These findings have several implications. 1) Discrete state networks reach optimal performance with weaker synapses; 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 after only a single activation per participating neuron; demonstrating event-based computation with short latencies. 4) The chaotic sub-manifold predicts a transient increase of variability after stimulus onset. Our results thus provide a hitherto unknown link between recurrent and chaotic dynamics of functional networks, neuronal variability, and dimensionality of neuronal responses.
000885628 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000885628 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x1
000885628 536__ $$0G:(DE-82)EXS-SF-neuroIC002$$aneuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002)$$cEXS-SF-neuroIC002$$x2
000885628 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x3
000885628 7001_ $$0P:(DE-Juel1)164473$$aKühn, Tobias$$b1
000885628 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b2$$ufzj
000885628 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b3$$eLast author$$ufzj
000885628 773__ $$p2002.11006
000885628 8564_ $$uhttps://juser.fz-juelich.de/record/885628/files/arXiv%20preprint.PDF$$yOpenAccess
000885628 909CO $$ooai:juser.fz-juelich.de:885628$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000885628 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171384$$aForschungszentrum Jülich$$b0$$kFZJ
000885628 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156459$$aForschungszentrum Jülich$$b2$$kFZJ
000885628 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b3$$kFZJ
000885628 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
000885628 9141_ $$y2020
000885628 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000885628 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000885628 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000885628 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000885628 9801_ $$aFullTexts
000885628 980__ $$apreprint
000885628 980__ $$aVDB
000885628 980__ $$aUNRESTRICTED
000885628 980__ $$aI:(DE-Juel1)INM-6-20090406
000885628 980__ $$aI:(DE-Juel1)IAS-6-20130828
000885628 980__ $$aI:(DE-Juel1)INM-10-20170113
000885628 981__ $$aI:(DE-Juel1)IAS-6-20130828