作为另一种神经形态学概念的二维形状的扫描转录
Scan transcription of two-dimensional shapes as an alternative neuromorphic concept
摘要：
塞尔弗里奇，以及萨瑟兰和马尔提供了一些最早的建议，如何编程计算机识别形状。Hubel和Wibel的诺贝尔奖获得者发现初级视觉皮层中的神经元选择性地响应轮廓方向的函数，从而加强了他们对轮廓特征的过滤，特别是边界段的方向。无数的研究人员和理论家继续在这一方法的基础上进行研究。这些模型通常被描述为神经形态的，这意味着计算方法是基于生物学上合理的原则。本实验室最近的工作对强调方向选择性和使用神经网络原理提出了挑战。本报告的目标不是重新讨论这些问题，而是提供一种替代概念，用于编码可能对神经形态建模者有用的形状信息。
英文摘要：
Selfridge, along with Sutherland and Marr provided some of the earliest proposals for how to program computers to recognize shapes. Their emphasis on filtering for contour features, especially the orientation of boundary segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who discovered that neurons in primary visual cortex selectively respond as a function of contour orientation. Countless investigators and theorists have continued to build on this approach. These models are often described as neuromorphic, which implies that the computational methods are based on biologically plausible principles. Recent work from the present lab has challenged the emphasis on orientation selectivity and the use of neural network principles. The goal of the present report is not to relitigate those issues, but to provide an alternative concept for encoding of shape information that may be useful to neuromorphic modelers.
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。
