Modification of Holographic Graph Neuron Using Sparse Distributed Representations

This article presents a modification of the recently proposed Holographic Graph Neuron approach for memorizing patterns of generic sensor stimuli. The original approach represents patterns as dense binary vectors, where zeros and ones are equiprobable. The presented modification employs sparse binary distributed representations where the number of ones is less than zeros. Sparse representations are more biologically plausible because activities of real neurons are sparse. Performance was studied comparing approaches for different sizes of dimensionality.