CRCNS: Attentional Selection and Perceptional Organization

描述(由申请人提供)：这项拟议的研究解决了视觉系统中知觉组织与注意选择如何相互作用的问题。虽然这些问题通常被认为是分开的，但我们认为它们是紧密相连的，实际上可能共享一个共同的神经基础。我们认为，图形-背景组织的神经机制，即视觉对象边界的神经表征，依赖于神经元回路，该回路也被用来表示这些对象是否有人参与。我们将研究纹状体外皮质中多个电极的单细胞活动。这些数据将被用来约束底层神经元电路的大规模详细模型。将追求三个具体目标。第一个目标是模拟大脑皮层V2区与注意无关的图形-背景组织机制。在之前的工作中，我们开发了一个图形-背景分离模型，解释了边界所有权选择性的机制。该模型只能解释平均射速的变化。新模型将基于包括尖峰信号的单个神经元模型，因此将能够对边界所有权信号的幅度和时间过程以及神经元之间的成对尖峰序列关联进行建模。第二个目标是研究图形-背景结构的短期记忆。我们将在区域V2中执行多个同时的单单元记录，以表征最近在边界所有权编码中观察到的滞后效应。我们还将在较高的纹状外区(V3和V4)进行记录，因为边界所有权选择性的快速时间进程使其很可能是通过白质的联系传递的。这些电生理记录将与开发边界所有权信号的持续性和滞后性模型相补充。我们将通过引入更复杂的单神经元模型来扩展尖峰神经网络模型，该模型可以解释滞后效应的潜在机制。第三个目的是研究选择性注意与图形-背景组织和特征绑定机制之间的相互作用。我们认为纹外皮层中观察到的前景图形对侧面的选择性来自于细胞分组的反复偏向，后者也被用来专心地选择图形。我们将从纹外区V2的单个细胞和成对细胞进行记录，并研究选择性注意对边界所有权选择性的影响。这些录音将与自上而下的选择性注意与图形背景组织的交互作用模型相结合。我们将扩展在目标1下开发的脉冲神经网络模型，以包括选择性注意。该模型将解释在约束条件和注意状态变化下的速率效应和成对相关函数。这项拟议的研究将有助于我们理解灵长类视觉的一些最基本的机制，这对于理解人类的正常和受损的视觉都是重要的。从这个项目中获得的洞察力将有助于理解阅读困难和半忽略等认知障碍的神经基础。与公共卫生相关：在我们看来，看东西很容易。事实上，这是一个非常复杂的过程，从没有一台计算机在人工视觉方面的表现可以与简单的动物相提并论就可以看出这一点。这项拟议的研究的目标是了解视觉场景是如何被解剖成视觉对象的，以及这些视觉对象是如何被仔细地选择来进行更详细的处理的。注意选择的缺陷存在于许多神经系统疾病中，例如偏瘫，阐明选择性注意是如何与图像理解一起工作的，对于理解这些疾病的机制将是重要的。

DESCRIPTION (provided by applicant): The proposed research addresses the question of how perceptual organization interfaces with attentional selection in the visual system. While these questions are frequently considered separate, we believe that they are closely connected and may in fact share a common neural substrate. We propose that the neuronal mechanisms of figure-ground organization, that is, the neural representation of the borders of a visual object, relies on neuronal circuitry that is also used to represent whether these objects are attended or not. We will study single cell activity with multiple electrodes in extrastriate cortex. These data will be used to constrain large-scale detailed models of the underlying neuronal circuitry. Three specific aims will be pursued. The first Aim is to model the mechanisms of attention-independent figure-ground organization in cortical area V2. In previous work, we have developed a model of figure-ground segregation that explains mechanisms underlying border ownership selectivity. The model can only explain changes in mean firing rates. The new model will be based on a model of single neurons that includes spiking and will thus be able to model amplitude and time course of the border ownership signals as well as pair wise spike train correlation between neurons. The second Aim is to study short-term memory for figure-ground structure. We will perform multiple simultaneous single-unit recordings in area V2 to characterize the recently observed hysteresis effects in border ownership coding. We will also record in higher extrastriate areas (V3 and V4) since the fast time course of border ownership selectivity makes it likely that it is imparted by connections through the white matter. These electrophysiological recordings will be complemented with the development of a model of persistence and hysteresis of border ownership signals. We will expand the spiking neural network model by introducing more complex single-neuron models that can explain the mechanisms underlying the hysteresis effects. The third Aim is to study how selective attention interacts with mechanisms of figure-ground organization and feature binding. We suggest that the selectivity to side of foreground figure observed in extrastriate cortex arises from a recurrent bias from grouping cells, and that the latter are also used to attentively select the figure. We will record from single cells and pairs of cells in extrastriate area V2 and study the influence of selective attention on border ownership selectivity. These recordings will be combined with a model of the interaction of top-down selective attention with figure ground organization. We will expand the spiking neural network model developed under Aim 1 to include selective attention. The model will explain rate effects and pair wise correlation functions under a variation of binding conditions and attentional states. The proposed research will contribute to our understanding of some of the most fundamental mechanisms of primate vision which is of importance for understanding both normal and impaired vision in humans. The insight gained from this project will contribute to the understanding of the neural basis of cognitive disorders such as dyslexia and hemi-neglect. PUBLIC HEALTH RELEVANCE: It appears to us that seeing is easy. In reality, it is a very complex process, as can be seen by the fact that no computer has a performance in artificial vision comparable to even simple animals. The goal of the proposed research is to understand how a visual scene is dissected into visual objects, and how these visual objects are attentively selected for more detailed processing. Deficiencies in attentional selection are present in many neurological diseases, e.g. hemineglect, and elucidating how selective attention works with image understanding will be important for understanding the mechanisms underlying these diseases.