000889320 001__ 889320
000889320 005__ 20240313094932.0
000889320 037__ $$aFZJ-2021-00212
000889320 1001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b0$$eCorresponding author
000889320 1112_ $$aWWTH seminar series$$cParis$$wFrance
000889320 245__ $$aTransient chaotic dimensionality expansion by recurrent networks$$f2020-12-16 - 
000889320 260__ $$c2020
000889320 3367_ $$033$$2EndNote$$aConference Paper
000889320 3367_ $$2DataCite$$aOther
000889320 3367_ $$2BibTeX$$aINPROCEEDINGS
000889320 3367_ $$2ORCID$$aLECTURE_SPEECH
000889320 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1625842148_6946$$xInvited
000889320 3367_ $$2DINI$$aOther
000889320 520__ $$aTransient chaotic dimensionality expansion by recurrent networksMoritz HeliasINM-6, Juelich Research CentreFaculty of Physics, RWTH Aachen UniversityCortical neurons communicate with spikes, which are discrete events intime and value. They often show optimal computational performance close toa transition to rate-chaos; chaos that is driven by local and smooth averagesof the discrete activity.We here analyze microscopic and rate chaos in discretely-coupled networksof binary neurons by a model-independent field theory. We find a stronglynetwork size-dependent transition to microscopic chaos and a chaoticsubmanifold that spans only a finite fraction of the entire activity space.Rate chaos is shown to be impossible in these networks.Applying stimuli to a strongly microscopically chaotic binary networkthat acts as a reservoir, one observes a transient expansion of thedimensionality of the representing neuronal space. Crucially, the numberof dimensions corrupted by noise lags behind the informative dimensions.This translates to a transient peak in the networks' classification performanceeven deeply in the chaotic regime, extending the view that computationalperformance is always optimal near the edge of chaos. Classificationperformance peaks rapidly within one activation per neuron, demonstratingfast event-based computation. The generality of this mechanism isunderlined by simulations of spiking networks of leaky integrate-and fireneurons.1. Keup, Kuehn, Dahmen, Helias (2020) Transient chaotic dimensionality expansion by recurrent networks. arXiv:2002.11006 [cond-mat.dis-nn]
000889320 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x0
000889320 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1
000889320 909CO $$ooai:juser.fz-juelich.de:889320$$pVDB
000889320 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b0$$kFZJ
000889320 9130_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x0
000889320 9130_ $$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$$x1
000889320 9141_ $$y2021
000889320 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000889320 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000889320 980__ $$atalk
000889320 980__ $$aVDB
000889320 980__ $$aI:(DE-Juel1)INM-6-20090406
000889320 980__ $$aI:(DE-Juel1)IAS-6-20130828
000889320 980__ $$aUNRESTRICTED
000889320 981__ $$aI:(DE-Juel1)IAS-6-20130828