Holographic associative memory

From Wikipedia, the free encyclopedia

The introduction to this article provides insufficient context for those unfamiliar with the subject matter.
Please help improve the introduction to meet Wikipedia's layout standards. You can discuss the issue on the talk page.

This article or section is in need of attention from an expert on the subject.
WikiProject Computing may be able to help recruit one.
If a more appropriate WikiProject or portal exists, please adjust this template accordingly.

Holographic Associative Memory is part of the family of analog, correlation-based, associative, stimulus-response memories, where information is mapped onto the phase orientation of complex numbers operating. It can be considered as a complex valued artificial neural network. The holographic associative memory exhibits some remarkable characteristics. Holographs have been shown to be effective for associative memory tasks, generalization, and pattern recognition with changeable attention. Ability of dynamic search localization is central to natural memory. For example, in visual perception, human always tends to focus on some specific objects in the pattern. Humans can effortlessly change the focus from object to object without requiring relearning. It provides a computational model which can mimic this ability by creating representation for focus. At the heart of this new memory lie a novel bi-modal representation of pattern, and a hologram like complex spherical weight state-space. Besides the usual advantages of associative computing, this technique also has excellent potential for fast optical realization, because the underlying hyper-spherical computations can be naturally implemented on optical computations.

[edit] See also

Holographic Memory

[edit] Bibliography

Sutherland, J., Holographic Models of Memory, learning and Expression, International J. Of Neural Systems, 1(3), 1990, pp356-267

J. I. Khan. Attention Modulated Associative Computing and Content-Associative Search in Image Archive. PhD thesis, University of Hawaii, August 1995.

K. I. Khan and D. Y. Yun. Characteristics of Multidimensional Holographic Associative Memory in Retrieval with Dynamically Localizable Attention. IEEE Transactions on Neural Networks, 9(3):389–406, May 1998.

HE Michel, AAS Awwal, Enhanced artificial neural networks using complex numbers , Neural Networks, 1999. Proceedings. 1999 IEEE International Joint Conference on

R Stoop, J Buchli, G Keller, WH Steeb, Stochastic resonance in pattern recognition by a holographic neuron model, Physical Review E, 2003.

Y Hendra, RP Gopalan, MG Nair, A method for dynamic indexing of large image databases, Systems, Man, and Cybernetics, 1999. IEEE SMC'99.

HE Michel, S Kunjithapatham, Processing Landsat TM data using complex-valued neural networks, Proceedings of SPIE, the International Society for Optical, 2002.

RP Gopalan, G Lee , Indexing of Image Databases Using Untrained 4D Holographic Memory Model, 15th Australian Joint Conference on Artificial Intelligence, - Springer Page 1. RI McKay and J. Slaney (Eds.): AI 2002, LNAI 2557, pp. 237–248.