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Geometrical Computations Explain Projection Patterns of Long-Range Horizontal Connections in Visual Cortex

机译:几何计算解释视觉皮层中远程水平连接的投影模式

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摘要

Neurons in primary visual cortex respond selectively to oriented stimuli such as edges and lines. The long-range horizontal connections between them are thought to facilitate contour integration. While many physiological and psychophysical findings suggest that collinear or association field models of good continuation dictate particular projection patterns of horizontal connections to guide this integration process, significant evidence of interactions inconsistent with these hypotheses is accumulating. We first show that natural random variations around the collinear and association field models cannot account for these inconsistencies, a fact that motivates the search for more principled explanations. We then develop a model of long-range projection fields that formalizes good continuation based on differential geometry. The analysis implicates curvature(s) in a fundamental way, and the resulting model explains both consistent data and apparent outliers. It quantitatively predicts the (typically ignored) spread in projection distribution, its nonmonotonic variance, and the differences found among individual neurons. Surprisingly, and for the first time, this model also indicates that texture (and shading) continuation can serve as alternative and complementary functional explanations to contour integration. Because current anatomical data support both (curve and texture) integration models equally and because both are important computationally, new testable predictions are derived to allow their differentiation and identification.
机译:初级视觉皮层中的神经元选择性地响应定向刺激,例如边缘和线条。它们之间的远程水平连接被认为有助于轮廓集成。尽管许多生理和心理物理学发现表明,良好连续性的共线或关联场模型决定了水平连接的特定投影模式,以指导这一整合过程,但与这些假设不一致的相互作用的大量证据正在积累。我们首先表明,共线和关联场模型周围的自然随机变化不能解决这些不一致问题,这一事实促使人们寻求更多有原则的解释。然后,我们建立一个远程投影场的模型,该模型基于微分几何来形式化良好的连续性。分析从根本上暗示了曲率,结果模型解释了一致的数据和明显的异常值。它定量地预测了投影分布中的扩散(通常被忽略),其非单调方差以及各个神经元之间的差异。出人意料的是,这也是模型首次表明纹理(和阴影)的连续性可以作为轮廓整合的替代和补充功能说明。由于当前的解剖数据均支持(曲线和纹理)集成模型,并且两者都在计算上很重要,因此得出了新的可测试预测,以便对其进行区分和识别。

著录项

  • 来源
    《Neural computation》 |2004年第3期|p.445-476|共32页
  • 作者

    Ohad Ben-Shahar; Steven Zucker;

  • 作者单位

    Department of Computer Science and the Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, U.S.A.;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
  • 关键词

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