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Holographic Implementation Of A Discrete Hopfield Neural Network Model

This paper investigates the feasibility of constructing a Hopfield neural network model using optical techniques. The use of thin holographic elements to form the weighted interconnections is discussed as well as the desirable response of 'nonlinear processing elements. This implementation is of a bipolar vector model and processes the negative numbers involved without using a subtraction stage. Our analysis suggests that although present day technology would limit the size of the network that could be implemented, the construction of an optical machine is now possible with a connectivity exceeding any curently available electronic machine.