title: Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing.
abstract: 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.
authors.forename: Denis
authors.initials: D
authors.lastname: Kleyko
authors.forename: Evgeny
authors.initials: E
authors.lastname: Osipov
authors.forename: Alexander
authors.initials: A
authors.lastname: Senior
authors.forename: Asad I
authors.initials: AI
authors.lastname: Khan
authors.forename: Yasar Ahmet
authors.initials: YA
authors.lastname: Sekercioglu