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001024857 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02523
001024857 037__ $$aFZJ-2024-02523
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001024857 1001_ $$0P:(DE-Juel1)201426$$aRenner, Alpha$$b0$$eCorresponding author$$ufzj
001024857 1112_ $$aInternational conference on neuromorphic, natural and physical computing$$cHannover$$d2023-10-25 - 2023-10-27$$gNNPC2023$$wGermany
001024857 245__ $$aNeuromorphic Hyperdimensional Visual Scene Factorization
001024857 260__ $$c2023
001024857 3367_ $$033$$2EndNote$$aConference Paper
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001024857 520__ $$aIn this talk at NNPC 2023, I will present a modular neuromorphic algorithm leveraging recent advancements in hyperdimensional computing/ Vector Symbolic Architectures (VSAs).VSAs have been proposed as a framework for symbolic reasoning, spatial, and graph operations on neuromorphic hardware. They make use of a small set of computational primitives that are robust, efficient, and compatible with diverse hardware. Our algorithm approaches scene understanding as a factorization problem and employs the resonator network to extract object identities and transformations. This is achieved by reflecting the algebraic structure of 2d rigid transforms (translations and rotation) in the neural VSA representation. Finally, we use a spike-timing-based implementation of phasor neurons to show an efficient proof of concept implementation on neuromorphic hardware and employ the model in a robotics task for visual odometry (visual SLAM).
001024857 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001024857 7001_ $$0P:(DE-HGF)0$$aSupic, Lazar$$b1
001024857 7001_ $$0P:(DE-HGF)0$$aDanielescu, Andreea$$b2
001024857 7001_ $$0P:(DE-HGF)0$$aIndiveri, Giacomo$$b3
001024857 7001_ $$0P:(DE-HGF)0$$aOlshausen, Bruno A.$$b4
001024857 7001_ $$0P:(DE-HGF)0$$aSandamirskaya, Yulia$$b5
001024857 7001_ $$0P:(DE-HGF)0$$aSommer, Friedrich T.$$b6
001024857 7001_ $$0P:(DE-HGF)0$$aPaxon Frady, E.$$b7
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001024857 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)201426$$aForschungszentrum Jülich$$b0$$kFZJ
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