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001006605 005__ 20240313095011.0
001006605 037__ $$aFZJ-2023-01742
001006605 041__ $$aEnglish
001006605 1001_ $$0P:(DE-Juel1)174585$$aNestler, Sandra$$b0$$eCorresponding author$$ufzj
001006605 1112_ $$aSeminar Talk, Barak Lab$$cHaifa$$wIsrael
001006605 245__ $$aNeural networks learning structure in temporal signals$$f2023-02-28 - 
001006605 260__ $$c2023
001006605 3367_ $$033$$2EndNote$$aConference Paper
001006605 3367_ $$2DataCite$$aOther
001006605 3367_ $$2BibTeX$$aINPROCEEDINGS
001006605 3367_ $$2ORCID$$aLECTURE_SPEECH
001006605 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1683183034_28115$$xInvited
001006605 3367_ $$2DINI$$aOther
001006605 520__ $$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.
001006605 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001006605 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001006605 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x2
001006605 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
001006605 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x4
001006605 536__ $$0G:(DE-Juel-1)BMBF-01IS19077A$$aRenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A)$$cBMBF-01IS19077A$$x5
001006605 536__ $$0G:(DE-Juel-1)PF-JARA-SDS005$$aSDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005)$$cPF-JARA-SDS005$$x6
001006605 536__ $$0G:(DE-82)EXS-SF-neuroIC002$$aneuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002)$$cEXS-SF-neuroIC002$$x7
001006605 7001_ $$0P:(DE-Juel1)171384$$aKeup, Christian$$b1$$ufzj
001006605 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b2$$ufzj
001006605 7001_ $$0P:(DE-Juel1)184621$$aGilson, Matthieu$$b3
001006605 7001_ $$0P:(DE-HGF)0$$aRauhut, Holger$$b4
001006605 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$ufzj
001006605 7001_ $$0P:(DE-HGF)0$$aBartolomaeus, Wiebke$$b6
001006605 7001_ $$0P:(DE-HGF)0$$aBoutaib, Youness$$b7
001006605 7001_ $$0P:(DE-Juel1)178725$$aBouss, Peter$$b8$$ufzj
001006605 7001_ $$0P:(DE-Juel1)184900$$aMerger, Claudia Lioba$$b9$$ufzj
001006605 7001_ $$0P:(DE-Juel1)180150$$aFischer, Kirsten$$b10$$ufzj
001006605 7001_ $$0P:(DE-Juel1)178936$$aRene, Alexandre$$b11$$ufzj
001006605 7001_ $$0P:(DE-HGF)0$$aSchirrmeister, Robin$$b12
001006605 7001_ $$0P:(DE-HGF)0$$aBall, Tonio$$b13
001006605 909CO $$ooai:juser.fz-juelich.de:1006605$$pec_fundedresources$$pVDB$$popenaire
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001006605 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$$x0
001006605 9131_ $$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
001006605 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$$x2
001006605 9141_ $$y2023
001006605 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001006605 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001006605 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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