Spatial Integration of V1 Horizontal Disparity Signals

描述（由申请人提供）：我们将测试这样的假设，即视觉系统使用初级视觉皮层中的神经元之间的合作处理来匹配左眼图像和右眼图像之间的特征，所述初级视觉皮层对这些图像之间的水平差异做出特异性响应。神经元之间的有效连接使用尖峰序列之间的互相关来量化。我们将研究动态随机点立体图中呈现的深度变化过程中这些有效连接的动态，并将其与合作算法的预测进行比较。此外，我们将测试的假设，合作机制也有利于检测方向（深度）的表面。我们将通过测量有效连接的动态来扩展我们的方法，同时显示动态随机点立体图梯度。表面定向是检测更复杂的基于深度的表面图案的基本步骤。拟议项目的结果将提供有关神经整合的基本机制的基本细节。了解早期视差信号是如何整合的，将使我们了解复杂的深度感知如何通过视觉系统演变。这个项目的一般动机是了解神经元如何相互作用，这是确定大脑功能的基础。更具体地说，弄清楚视觉系统如何从二维图像中计算深度，对于开发已知深度感知缺陷的神经病理学治疗至关重要。

DESCRIPTION (provided by applicant): We will test the hypothesis that the visual system matches features between the left- and right-eye images using cooperative processing among neurons in primary visual cortex that respond specifically to horizontal disparities between these images. Effective connections between neurons are quantified using cross-correlation between spike trains. We will examine the dynamics of these effective connections during changes in depth rendered in dynamic random dot stereograms and compare them to predictions made by cooperative algorithms. In addition, we will test the hypothesis that cooperative mechanisms also facilitate detecting orientation (in depth) of a surface. We will extend our approach by measuring the dynamics of effective connections while displaying dynamic random dot stereogram gradients. Surface orientation is an elementary step in detecting more complex depth-based patterns of surfaces. The results of the proposed project will provide fundamental details about the underlying mechanisms of neural integration. Understanding how early disparity signals are integrated will allow us to understand how complex depth perception evolves through the visual system. A general motivation of this project is to understand how neurons interact, which is fundamental to determining how the brain functions. More specifically, figuring out how the visual system computes depth from two-dimensional images will be essential in developing treatment for neuropathology with known depth perception deficiencies.