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Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing
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DOI：10.1109/TNNLS.2016.2535338.png)
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摘要
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In this paper, we propose a new approach to implementing hierarchical graph neuron (HGN), an architecture for memorizing patterns of generic sensor stimuli, through the use of vector symbolic architectures. The adoption of a vector symbolic representation ensures a single-layer design while retaining the existing performance characteristics of HGN. This approach significantly improves the noise resistance of the HGN architecture, and enables a linear (with respect to the number of stored entries) time search for an arbitrary subpattern.
Keyword:
Associative memory (AM)
holographic graph neuron (HoloGN)
hyperdimensional computing
pattern recognition
vector symbolic architectures (VSAs)
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8.9
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7.5K
被引数:
7.2W
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