神经形态形状滤波器设计的进化观点
An evolutionary perspective on the design of neuromorphic shape filters
摘要：
大量的时间和精力已经投入到使用连接主义（神经网络）原理开发机器视觉。大部分的工作都受到了神经科学家和行为学家关于皮层系统如何存储刺激信息的理论的启发。这些理论要求信息通过几个神经元群体之间的连接流动，初始连接是随机的（或至少是非功能性的）。然后，通过训练试验来修改连接的强度或位置，以实现有效的输出，例如识别物体的能力。这些理论忽略了一个事实，即没有皮层的动物，鱼，可以展示出超越最好的神经网络模型的视觉技能。允许立即有效的视觉和快速学习的神经回路已经通过数亿年的进化预先编程，并且视觉技能在孵化后不久即可获得。皮质系统可能提供先进的图像处理，但最有可能的是使用在简单系统中已被证明有效的设计原则。本文简要介绍了视网膜和皮层机制注册形状信息，希望它可能有助于形状编码电路的设计，更紧密地匹配生物视觉的机制。
英文摘要：
A substantial amount of time and energy has been invested to develop machine vision using connectionist (neural network) principles. Most of that work has been inspired by theories advanced by neuroscientists and behaviorists for how cortical systems store stimulus information. Those theories call for information flow through connections among several neuron populations, with the initial connections being random (or at least non-functional). Then the strength or location of connections are modified through training trials to achieve an effective output, such as the ability to identify an object. Those theories ignored the fact that animals that have no cortex, e.g., fish, can demonstrate visual skills that outpace the best neural network models. Neural circuits that allow for immediate effective vision and quick learning have been preprogrammed by hundreds of millions of years of evolution and the visual skills are available shortly after hatching. Cortical systems may be providing advanced image processing, but most likely are using design principles that had been proven effective in simpler systems. The present article provides a brief overview of retinal and cortical mechanisms for registering shape information, with the hope that it might contribute to the design of shape-encoding circuits that more closely match the mechanisms of biological vision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
