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000889332 037__ $$aFZJ-2021-00223
000889332 1001_ $$0P:(DE-Juel1)174585$$aNestler, Sandra$$b0$$eCorresponding author$$ufzj
000889332 1112_ $$a34th Conference on Neural Information Processing Systems$$conline$$d2020-12-06 - 2020-12-12$$gNeurIPS 2020$$wonline
000889332 245__ $$aUnfolding recurrence by Green’s functions for optimized reservoir computing
000889332 260__ $$c2020
000889332 300__ $$a1
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000889332 520__ $$aCortical networks are strongly recurrent,  and neurons have intrinsic temporaldynamics.  This sets them apart from deep feed-forward networks.  Despite thetremendous progress in the application of feed-forward networks and their the-oretical understanding, it remains unclear how the interplay of recurrence andnon-linearities in recurrent cortical networks contributes to their function.  Thepurpose of this work is to present a solvable recurrent network model that links tofeed forward networks. By perturbative methods we transform the time-continuous,recurrent dynamics into an effective feed-forward structure of linear and non-lineartemporal kernels. The resulting analytical expressions allow us to build optimaltime-series classifiers from random reservoir networks. Firstly, this allows us tooptimize not only the readout vectors, but also the input projection, demonstratinga strong potential performance gain. Secondly, the analysis exposes how the secondorder stimulus statistics is a crucial element that interacts with the non-linearity ofthe dynamics and boosts performance.
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000889332 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000889332 536__ $$0G:(DE-82)EXS-SF-neuroIC002$$aneuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002)$$cEXS-SF-neuroIC002$$x2
000889332 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x3
000889332 536__ $$0G:(DE-Juel-1)BMBF-01IS19077A$$aRenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A)$$cBMBF-01IS19077A$$x4
000889332 536__ $$0G:(DE-Juel-1)PF-JARA-SDS005$$aSDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005)$$cPF-JARA-SDS005$$x5
000889332 7001_ $$0P:(DE-Juel1)171384$$aKeup, Christian$$b1$$ufzj
000889332 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b2$$ufzj
000889332 7001_ $$0P:(DE-Juel1)184621$$aGilson, Matthieu$$b3$$ufzj
000889332 7001_ $$0P:(DE-HGF)0$$aRauhut, Holger$$b4
000889332 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$ufzj
000889332 8564_ $$uhttps://juser.fz-juelich.de/record/889332/files/neurips_accepted.pdf$$yOpenAccess
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000889332 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
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