CRCNS: Attentional Selection and Perceptional Organization

CRCNS：注意选择和感知组织

• 批准号: 8132314
• 负责人: ERNST NIEBUR
• 金额: $ 38.75万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8132314
• 关键词: Address; Animals; Area; Attention; Binding; Buffers; Cells; Code; Cognition Disorders; Cognitive; Complement; Complex; Computers; Data; Dependence; Development; Disease; Dyslexia; Electrodes; Goals; Grouping; Health; Human; Image; Indium; Individual; Modeling; Monkeys; Neural Network Simulation; Neurons; Ownership; Performance; Primates; Process; Recurrence; Research; Sensory; Short-Term Memory; Side; Signal Transduction; Stimulus; Structure; Theoretical Studies; Time; Training; Variant; Vision; Visual; Visual Perception; Visual system structure; Work; area V2; area V3; awake; base; extrastriate visual cortex; insight; neglect; nervous system disorder; neuromechanism; neuronal circuitry; nonhuman primate; perceptual organization; relating to nervous system; research study; response; segregation; selective attention; white matter

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.

描述（由申请人提供）：拟议的研究解决了感知组织如何与视觉系统中的注意力选择相互作用的问题。虽然这些问题经常被认为是独立的，但我们相信它们是密切相关的，并且实际上可能共享一个共同的神经基础。我们提出，图形-背景组织的神经元机制，即视觉对象边界的神经表示，依赖于神经元电路，该神经元电路也用于表示这些对象是否被关注。我们将使用纹外皮质中的多个电极研究单细胞活动。这些数据将用于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。第一个目标是模拟皮质区 V2 中与注意力无关的图形-背景组织机制。在之前的工作中，我们开发了一个图形-背景隔离模型，该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放电率的变化。新模型将基于包含尖峰的单个神经元模型，因此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相关性进行建模。第二个目标是研究图形-背景结构的短期记忆。我们将在 V2 区域执行多个同时单单元记录，以表征最近观察到的边界所有权编码中的滞后效应。我们还将在更高的外域区域（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连接传递的。这些电生理记录将通过边界所有权信号的持久性和滞后性模型的开发得到补充。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，这些模型可以解释滞后效应的机制。第三个目标是研究选择性注意如何与图形-背景组织和特征绑定机制相互作用。我们认为，在纹状外皮层中观察到的前景图形侧面的选择性源于分组细胞的反复偏差，并且后者也用于仔细选择图形。我们将从域外区域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。这些记录将与自上而下选择性注意与图形地面组织相互作用的模型相结合。我们将扩展目标 1 下开发的尖峰神经网络模型，以包含选择性注意。该模型将解释不同绑定条件和注意力状态下的速率效应和成对相关函数。拟议的研究将有助于我们了解灵长类动物视觉的一些最基本机制，这对于了解人类正常和受损的视力非常重要。从该项目中获得的见解将有助于理解阅读障碍和偏侧忽视等认知障碍的神经基础。公共卫生相关性：在我们看来，看到东西很容易。事实上，这是一个非常复杂的过程，从没有计算机的人工视觉性能可以与简单动物相媲美的事实可以看出。本研究的目的是了解如何将视觉场景分解为视觉对象，以及如何精心选择这些视觉对象以进行更详细的处理。许多神经系统疾病都存在注意力选择缺陷，例如神经系统疾病。半忽视，并阐明选择性注意如何与图像理解相结合，对于理解这些疾病的机制非常重要。