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注意选择的问题。尽管这些问题经常被认为是单独的，但我们认为它们是密切相关的，实际上可能具有共同的神经底物。我们建议，图形组织的神经元机制，即视觉对象边界的神经表示，依赖于神经元电路，该电路也用于表示是否参加了这些对象。我们将研究单细胞活性在腹腔外皮层中的多个电极。这些数据将用于约束基础神经元电路的大规模详细模型。将追求三个具体目标。第一个目的是建模皮质区域V2中与众不同的图形组织的机制。在以前的工作中，我们开发了一个图形隔离模型，该模型解释了边境所有权选择性的基础机制。该模型只能解释平均点火率的变化。新模型将基于一个包括尖峰的单个神经元的模型，因此能够对边界所有权信号的振幅和时间过程进行建模以及神经元之间的配对尖峰列车相关性。第二个目的是研究用于图形结构的短期记忆。我们将在区域V2中执行多个同时单位录音，以表征边境所有权编码中最近观察到的磁滞效应。我们还将在较高的外部区域（V3和V4）记录，因为边境所有权选择性的快速时间课程使其可能通过白色物质的连接来赋予它。这些电生理记录将与边境所有权信号的持久性和滞后模型的发展相辅相成。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作用的基础机制。第三个目的是研究选择性注意力如何与图形组织的机制相互作用和特征结合。我们建议，在腹腔外皮质中观察到的前景图的选择性来自分组细胞的复发偏置，后者也用于认真选择该图。我们将从腹腔外区域V2中的单个细胞和成对的细胞记录，并研究选择性关注对边境所有权选择性的影响。这些记录将与自上而下的选择性关注与图形组织的相互作用的模型结合使用。我们将扩展AIM 1下开发的尖峰神经网络模型，以包括选择性关注。该模型将在结合条件和注意状态的变化下解释速率效应并配对明智的相关函数。拟议的研究将有助于我们理解灵长类动物视野的一些最基本的机制，这对于理解人类正常和受损的视力至关重要。从该项目中获得的洞察力将有助于理解诸如阅读障碍和hemi-neglect的认知障碍的神经基础。公共卫生相关性：在我们看来，看到很容易。实际上，这是一个非常复杂的过程，可以看出，没有计算机在人工视觉中具有与简单动物相当的人造视力的性能。拟议的研究的目的是了解如何将视觉场景分解为视觉对象，以及如何认真选择这些视觉对象以进行更详细的处理。注意力选择的缺陷存在于许多神经系统疾病中，例如Hemineglect，并阐明选择性注意力与图像理解的作用对于理解这些疾病背后的机制至关重要。