Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern
  Processing
  This article proposes the use of Vector Symbolic Architectures for
implementing Hierarchical Graph Neuron, an architecture for memorizing patterns
of generic sensor stimuli. The adoption of a Vector Symbolic representation
ensures a one-layered design for the approach, while maintaining the previously
reported properties and performance characteristics of Hierarchical Graph
Neuron, and also improving the noise resistance of the architecture. The
proposed architecture enables a linear (with respect to the number of stored
entries) time search for an arbitrary sub-pattern.
