Optical neural computing for associative memories

Citation

Hsu, Ken Yuh (1990) Optical neural computing for associative memories. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/9569-w314. https://resolver.caltech.edu/CaltechETD:etd-05042007-142242

Abstract

Optical techniques for implementing neural computers are presented. In particular, holographic associative memories with feedback are investigated. Characteristics of optical neurons and optical interconnections are discussed. An LCLV is used for simulating a 2-D array of approximately 160,000 optical neurons. Thermoplastic plates are used for providing holographic interconnections among these neurons. The problem of degenerate readout in holographic interconnections and the method of sampling grids to solve this problem are presented.

Two optical neural networks for associative memories are implemented and demonstrated. The first one is an optical implementation of the Hopfield network. It performs the function of auto-association that recognizes 2-D images from a distorted or partially blocked input. The trade-off between distortion tolerance and discrimination capability against new images is discussed. The second optical loop is a 2-layer network with feedback. It performs the function of hetero-association, which locks the recognized input and its associated image as a stable state in the loop. In both optical loops, it is shown that the neural gain and the similarity between the input and the stored images are the main factors that determine the dynamics of the network.

Neural network models for the optical loops are presented. Equations of motion for describing the dynamical behavior of the systems are derived. The reciprocal vector basis corresponding to stored images is derived. A geometrical method is then introduced which allows us to inspect the convergence property of the system. It is also shown that the main factors that determine the system dynamics are the neural gain and the initial conditions.

Photorefractive holography for optical interconnections and sampling grids for volume holographic interconnections are presented. A periodic copying method for refreshening multiply exposured photorefractive holograms is presented, which allows the hologram to maintain the same diffraction efficiency as that when a single exposure scheme is used. This scheme provides us with the possibility of achieving maximum storage and maximum diffraction efficiency in holographic associative memories.

Thesis Files