Optical associative memory model based on neural networks having variable interconnection weights

A new model is proposed for a content-addressable memory (CAM) based on neural networks. Like the previous Hopfield model, the information is stored in the structure of the network and the read-out procedure may be implemented in the form of an optical vector-matrix multiplier. This model introduces intermediate layers of interneurons between the neuron layers and a dependence of the interconnection weights to a given neuron on the previous history of the neuron. The storage prescription allows each matrix element to have three values instead of only two as in the previous Hopfield model. This more complex model gives better results than the Hopfield model.