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024 7 _ |a 10.34734/FZJ-2024-02523
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037 _ _ |a FZJ-2024-02523
041 _ _ |a English
100 1 _ |a Renner, Alpha
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111 2 _ |a International conference on neuromorphic, natural and physical computing
|g NNPC2023
|c Hannover
|d 2023-10-25 - 2023-10-27
|w Germany
245 _ _ |a Neuromorphic Hyperdimensional Visual Scene Factorization
260 _ _ |c 2023
336 7 _ |a Conference Paper
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336 7 _ |a Other
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520 _ _ |a In 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).
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700 1 _ |a Supic, Lazar
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700 1 _ |a Danielescu, Andreea
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700 1 _ |a Indiveri, Giacomo
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700 1 _ |a Olshausen, Bruno A.
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700 1 _ |a Sandamirskaya, Yulia
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700 1 _ |a Sommer, Friedrich T.
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700 1 _ |a Paxon Frady, E.
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|v Neuromorphic Computing and Network Dynamics
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