12 January 1993 Optoelectronic neural network utilizing a joint transform correlator
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Adaptive Resonance Theory provides a neural network architecture for self-organizing arbitrary input patterns into stable categories. In our work we utilize a model of ART known as ART2-A, which is capable of processing both analog and binary patterns. Our model is adapted to handle 2-D images for input patterns, and to allow for translation invariance. The computation of image patterns is assisted by a joint transform correlator (JTC), providing a fast, real-time, translation-invariant method of initial comparison. The JTC has an advantage over other optical correlator architectures in that a separate matched filter for each input need not be constructed. In this paper, we present a brief overview of the ART2-A algorithm and our optoelectronic implementation of this neural network model. This paper is based on work by Kane and Paquin submitted to IEEE Transactions on Neural Networks entitled 'POPART: Partial Optical ImPlementation of Adaptive Resonance Theory 2'.
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Marc J. Paquin, Marc J. Paquin, Jonathan S. Kane, Jonathan S. Kane, } "Optoelectronic neural network utilizing a joint transform correlator", Proc. SPIE 1772, Optical Information Processing Systems and Architectures IV, (12 January 1993); doi: 10.1117/12.140933; https://doi.org/10.1117/12.140933

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