This project focuses on implementing arrays of photodetectors and associated first-level sensory neural networks in monolithic integrated circuit format for purposes of applying the field of vision to engineering applications of advanced photodetectors and for producing a physical model that can be used in the study of multiplicative lateral inhibition in visual systems. The photodetectors and electronic circuitry are fabricated using gallium-arsenide technology to allow for upward compatibility with integrated optoelectronic systems and to allow the constructed detector assemblies to function in harsher operating environments. Additionally, the monolithic integration of an array of photodetectors with a first level neural network offers advantages in size, reliability, cost, and hence commercial applicability. Biological visual systems perform many demanding tasks that are far from the present capabilities of electronic hardware. The success of many biological systems can mostly be attributed to a large degree of pre-processing of the visual image at the most peripheral level of the sensory system. This pre-processing has been described by many models, one of which is multiplicative lateral inhibition, which has the advantage of simple implementation in electronic circuitry and is studied in this project. Since recognition is studied in a fully parallel context, advanced photonic sensors based upon this developing technology have the potential to approach the spatial recognition performance of biological retinas and to greatly surpass them in temporal performance of feature acquisition.
