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000873633 005__ 20240313094942.0
000873633 0247_ $$2doi$$a10.1109/NER.2019.8716891
000873633 0247_ $$2WOS$$aWOS:000469933200299
000873633 037__ $$aFZJ-2020-00876
000873633 1001_ $$0P:(DE-Juel1)171331$$aHe, Xu$$b0$$ufzj
000873633 1112_ $$a2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)$$cSan Francisco$$d2019-03-20 - 2019-03-23$$wCA
000873633 245__ $$aReservoir Transfer on Analog Neuromorphic Hardware
000873633 260__ $$c2019
000873633 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1580903721_16416
000873633 3367_ $$033$$2EndNote$$aConference Paper
000873633 3367_ $$2BibTeX$$aINPROCEEDINGS
000873633 3367_ $$2DRIVER$$aconferenceObject
000873633 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000873633 3367_ $$2ORCID$$aOTHER
000873633 500__ $$aTianlin Liu was supported by  the FZJ through the project SMARTSTART Computational Neuroscience, DB001423.
000873633 520__ $$aAnalog, unclocked, spiking neuromorphic microchips open new perspectives for implantable or wearable biosensors and biocontrollers, due to their low energy consumption and heat dissipation. However, the challenges from a computational point of view are formidable. Here we outline our solutions to realize the reservoir computing paradigm on such hardware and address the combined problems of low bit resolution, device mismatch, approximate neuron models, and timescale mismatch. The main contribution is a computational scheme, called Reservoir Transfer, which enables us to transfer the dynamical properties of a well-performing neural network which has been optimized on a digital computer, onto neuromorphic hardware that displays the abovementioned problematic properties. Here we present a case study of implementing an ECG heartbeat abnormality detector to showcase the proposed method.
000873633 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000873633 536__ $$0G:(EU-Grant)90251$$aSmartstart - SMARTSTART Training Program in Computational Neuroscience (90251)$$c90251$$x1
000873633 588__ $$aDataset connected to CrossRef Conference
000873633 7001_ $$0P:(DE-HGF)0$$aLiu, Tianlin$$b1
000873633 7001_ $$0P:(DE-HGF)0$$aHadaeghi, Fatemeh$$b2
000873633 7001_ $$0P:(DE-HGF)0$$aJaeger, Herbert$$b3
000873633 773__ $$a10.1109/NER.2019.8716891
000873633 909CO $$ooai:juser.fz-juelich.de:873633$$pec_fundedresources$$pVDB$$popenaire
000873633 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171331$$aForschungszentrum Jülich$$b0$$kFZJ
000873633 9131_ $$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$$x0
000873633 9141_ $$y2019
000873633 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000873633 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000873633 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000873633 980__ $$aabstract
000873633 980__ $$aVDB
000873633 980__ $$aI:(DE-Juel1)INM-6-20090406
000873633 980__ $$aI:(DE-Juel1)INM-10-20170113
000873633 980__ $$aI:(DE-Juel1)IAS-6-20130828
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000873633 981__ $$aI:(DE-Juel1)IAS-6-20130828