A single-layer neural network with 4×4 input neurons and 4 output neurons is optically implemented. Holographic lenslet arrays are used for the optical interconnection topology, a liquid crystal light valve (LCLV) is used for controlling optical interconnection weights. Using a Perceptron learning rule, it classifies input patterns into 4 different categories. It is shown that the performance of the adaptive neural network depends on the learning rate, the correlation of input patterns, and the non-linear charateristic properties of the liquid crystal light valve. Finally, the possible scheme of all optical perceptron using photorefractive crystal and liquid crystal light valve is demonstrated.
