000892802 001__ 892802
000892802 005__ 20240313095023.0
000892802 037__ $$aFZJ-2021-02359
000892802 041__ $$aEnglish
000892802 1001_ $$0P:(DE-Juel1)174585$$aNestler, Sandra$$b0$$eCorresponding author$$ufzj
000892802 1112_ $$aRNN seminar talk (online)$$cMortimer B. Zuckerman Mind Brain Behavior Institute$$wUSA
000892802 245__ $$aUnfolding recurrence by Green’s functions for optimized reservoir computing$$f2021-05-12 - 
000892802 260__ $$c2021
000892802 3367_ $$033$$2EndNote$$aConference Paper
000892802 3367_ $$2DataCite$$aOther
000892802 3367_ $$2BibTeX$$aINPROCEEDINGS
000892802 3367_ $$2ORCID$$aLECTURE_SPEECH
000892802 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1628519544_30837$$xInvited
000892802 3367_ $$2DINI$$aOther
000892802 520__ $$aCortical networks are strongly recurrent,  and neurons have intrinsic temporal dynamics.  This sets them apart from deep feed-forward networks.  Despite the tremendous progress in the application of feed-forward networks and their theoretical understanding, it remains unclear how the interplay of recurrence and non-linearities in recurrent cortical networks contributes to their function.  The purpose of this work is to present a solvable recurrent network model that links to feed forward networks. By perturbative methods we transform the time-continuous,recurrent dynamics into an effective feed-forward structure of linear and non-linear temporal kernels. The resulting analytical expressions allow us to build optimal time-series classifiers from random reservoir networks. Firstly, this allows us to optimize not only the readout vectors, but also the input projection, demonstrating a strong potential performance gain. Secondly, the analysis exposes how the second order stimulus statistics is a crucial element that interacts with the non-linearity of the dynamics and boosts performance.
000892802 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000892802 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000892802 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x2
000892802 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x3
000892802 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x4
000892802 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x5
000892802 7001_ $$0P:(DE-Juel1)171384$$aKeup, Christian$$b1$$ufzj
000892802 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b2$$ufzj
000892802 7001_ $$0P:(DE-Juel1)184621$$aGilson, Matthieu$$b3$$ufzj
000892802 7001_ $$0P:(DE-HGF)0$$aRauhut, Holger$$b4
000892802 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$ufzj
000892802 909CO $$ooai:juser.fz-juelich.de:892802$$pec_fundedresources$$pVDB$$popenaire
000892802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174585$$aForschungszentrum Jülich$$b0$$kFZJ
000892802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171384$$aForschungszentrum Jülich$$b1$$kFZJ
000892802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156459$$aForschungszentrum Jülich$$b2$$kFZJ
000892802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184621$$aForschungszentrum Jülich$$b3$$kFZJ
000892802 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b5$$kFZJ
000892802 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000892802 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
000892802 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x2
000892802 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$$vTheory, modelling and simulation$$x0
000892802 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$$vConnectivity and Activity$$x1
000892802 9141_ $$y2021
000892802 920__ $$lyes
000892802 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000892802 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000892802 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000892802 980__ $$atalk
000892802 980__ $$aVDB
000892802 980__ $$aI:(DE-Juel1)INM-6-20090406
000892802 980__ $$aI:(DE-Juel1)IAS-6-20130828
000892802 980__ $$aI:(DE-Juel1)INM-10-20170113
000892802 980__ $$aUNRESTRICTED
000892802 981__ $$aI:(DE-Juel1)IAS-6-20130828