An optoelectronic neural network

Mark A.A. Neil, Ian H. White, John E. Carroll

Research output: Contribution to journalArticlepeer-review


We describe and present results of an optoelectronic neural network processing system. The system uses an algorithm based on the Hebbian learning rule to memorise a set of associated vector pairs. Recall occurs by the processing of the input vector with these stored associations in an incoherent optical vector multiplier using optical polarisation rotating liquid crystal spatial light modulators to store the vectors and an optical polarisation shadow casting technique to perform multiplications. Results are detected on a photodiode arrayarray and thresholded electronically by a controlling microcomputer. The processor is shown to work in autoassociative and heteroassociative modes with up to 10 stored memory vectors of length 64 (equivalent to 64 neurons) and a cycle time of 50ms. We discuss the limiting factors at work in this system, how they affectaffect its scalability and the general applicability of its principles to other systems.

Original languageEnglish
Pages (from-to)409-417
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 5 Feb 1990

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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