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000893681 005__ 20240313094843.0
000893681 037__ $$aFZJ-2021-02753
000893681 1001_ $$0P:(DE-Juel1)181024$$aSchmidt, Marvin$$b0$$eCorresponding author$$ufzj
000893681 245__ $$aInvestigating the role of Chaos and characteristic time scales in Reservoir Computing$$f - 2021-06-14
000893681 260__ $$c2021
000893681 300__ $$a47 p.
000893681 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
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000893681 3367_ $$0PUB:(DE-HGF)19$$2PUB:(DE-HGF)$$aMaster Thesis$$bmaster$$mmaster$$s1636125049_17329
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000893681 502__ $$aMasterarbeit, RWTH Aachen University, 2021$$bMasterarbeit$$cRWTH Aachen University$$d2021
000893681 520__ $$aDynamical systems suited for Reservoir Computing (RC) should be able to both retain information for sufficiently long times and exhibit a rich representation of the input driving. However, selecting and tuning system parameters as well as choosing a sufficient input encoding has yet to be standardized as a procedure. This work attempts to make progress in this regard by focusing on the input and dynamical timescales in RC systems. Two qualitatively different models are studied: An adaptation of the Fermi-Pasta-Ulam-Tsingou model made suitable for Reservoir Computing and sparsely connected networks of spiking excitatory/inhibitory neurons. By comparing input injection frequencies to system relaxation timescales, and measuring its effects on the degree of chaos in the dynamical system, a relationship between timescales and the performance on a short term memory and parity-check tasks is established. We find that both systems rely on a close matching of their relaxation timescales with the input frequency in order to memorize and make precise use of the most recent information in the input. This was consistent across both models, implying greater generalizability. Furthermore, we find that a high degree of chaos deprecates memory in the Fermi-Pasta-Ulam-Tsingou model, while at the same time enhancing performance in the parity-check task, suggesting the edge of chaos to be an optimal tradeoff. The networks of spiking neurons show similar performance on the performance tasks, suggesting that nonlinear computations happen on a much faster timescale.
000893681 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000893681 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000893681 7001_ $$0P:(DE-Juel1)184684$$aPinna, Daniele$$b1$$eThesis advisor$$ufzj
000893681 7001_ $$0P:(DE-Juel1)171197$$aZajzon, Barna$$b2$$eThesis advisor$$ufzj
000893681 7001_ $$0P:(DE-Juel1)130848$$aMokrousov, Yuriy$$b3$$eThesis advisor$$ufzj
000893681 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b4$$eThesis advisor$$ufzj
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000893681 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181024$$aForschungszentrum Jülich$$b0$$kFZJ
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000893681 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$$x0
000893681 9131_ $$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$$vNeuromorphic Computing and Network Dynamics$$x1
000893681 9141_ $$y2021
000893681 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000893681 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000893681 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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