Sensory Neural Networks for Advanced Photodetectors

该项目侧重于以单片集成电路形式实现光电探测器阵列和相关的一级感觉神经网络，目的是将视野应用于先进光电探测器的工程应用，并产生可用于研究视觉系统中乘法侧抑制的物理模型。光电探测器和电子电路使用砷化镓技术制造，允许与集成光电系统向上兼容，并允许构建的探测器组件在更恶劣的操作环境中工作。此外，光电探测器阵列与一级神经网络的单片集成在尺寸、可靠性、成本和商业适用性方面具有优势。生物视觉系统执行许多苛刻的任务，远远不是目前电子硬件的能力。许多生物系统的成功主要归功于在感觉系统的最外围水平上对视觉图像进行了很大程度的预处理。这种预处理已经被许多模型所描述，其中一个是乘法横向抑制，它具有在电子电路中简单实现的优点，在本项目中进行了研究。由于识别是在完全并行的背景下研究的，基于这种发展技术的先进光子传感器有可能接近生物视网膜的空间识别性能，并在特征获取的时间性能上大大超过它们。

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.