Photorefractive adaptive resonance neural network

We describe a novel adaptive resonance theory (ART) device that is fully optical in the input-output processing path. This device is based on holographic information processing in a photorefractive crystal. This sets up an associative pattern retrieval in a resonating loop that uses angle-multiplexed reference beams for pattern classification. A reset mechanism is used to reject any given beam, permitting an ART search strategy. The design is similar to an existing nonlearning optical associative memory, but ours permits learning and makes use of information that the other device discards. It is a suitable response to the challenges of connectivity, learning, and reset presented by ART architectures. Furthermore, the design includes an efficient mechanism for area normalization of templates. It also permits the user to capitalize on the ability of ART networks to process large patterns. This new device is expected to offer higher information storage density than alternative ART implementations.