Phase Oscillatory Network and Visual Pattern Recognition

IEEE Trans Neural Netw Learn Syst. 2015 Jul;26(7):1539-44. doi: 10.1109/TNNLS.2014.2345572. Epub 2014 Aug 15.

Abstract

We explore a properly interconnected set of Kuramoto type oscillators that results in a new associative-memory network configuration, which includes second- and third-order additional terms in the Fourier expansion of the network's coupling. Investigation of the response of the network to different external stimuli indicates an increase in the network capability for coding and information retrieval. Comparison of the network output with that of an equivalent experiment with subjects, for recognizing perturbed binary patterns, shows comparable results between the two approaches. We also discuss the enhanced storage capacity of the network.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Color
  • Electronic Data Processing / methods*
  • Fourier Analysis
  • Information Storage and Retrieval
  • Memory
  • Neural Networks, Computer*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results