Spatial Integration of V1 Horizontal Disparity Signals

描述（由申请人提供）：我们将测试视觉系统通过初级视觉皮层神经元之间的合作处理来匹配左眼和右眼图像之间的特征的假设，这些神经元对这些图像之间的水平差异做出专门的反应。神经元之间的有效连接使用脉冲序列之间的相互关系进行量化。我们将研究在动态随机点立体图中呈现的深度变化期间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，我们将测试合作机制也有助于检测表面方向（深度）的假设。我们将通过在显示动态随机点立体图梯度的同时测量有效连接的动态来扩展我们的方法。表面定向是检测更复杂的基于深度的表面模式的基本步骤。该计划的结果将提供有关神经整合潜在机制的基本细节。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是如何通过视觉系统进化的。这个项目的总体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.