Tschechne Stephan, Neumann Heiko
Faculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.
Front Comput Neurosci. 2014 Aug 11;8:93. doi: 10.3389/fncom.2014.00093. eCollection 2014.
Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.
环境中的视觉结构被分割为图像区域，并将这些区域组合成表面和原型物体的表征。这种感知组织是由灵长类动物视觉皮层中的复杂神经机制执行的。腹侧皮质通路中的多个相互连接的区域接收视觉输入，并提取局部形状特征，这些特征随后被分组为越来越复杂、更有意义的图像元素。这样一个分布式处理网络必须能够捕捉形状边界的高度清晰变化以及对物体感知有贡献的非常细微的曲率变化。我们提出一种循环计算网络架构，该架构利用形状特征的分层分布式表征来编码不同分辨率尺度上的表面和物体边界。我们的模型利用了模拟视觉皮层早期和中间阶段（即V1 - V4区和IT区）处理能力的神经机制。我们认为多个专门的成分表征通过前馈分层处理相互作用，这种处理与由更高阶段生成的表征驱动的反馈信号相结合。基于此，全局配置信息以及局部信息可用于区分物体轮廓的变化。一旦确定了形状的轮廓，上下文轮廓配置就用于指定边界所有权方向，从而实现图形与背景的分离。因此，该模型提出了不同机制如何对分布式分层皮质形状表征做出贡献，并与图形 - 背景分离过程相结合。我们用一系列刺激对模型进行测试，以说明不同处理阶段的处理结果。我们特别强调调制性反馈连接如何在处理层次结构的各个阶段对视觉输入的处理做出贡献。
