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Laminar Neural Field Models for the Visual Cortex of the Brain

大脑视觉皮层的层状神经场模型

基本信息

批准号：
1613048
负责人：
Paul Bressloff
金额：
$ 40万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2020-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613048&HistoricalAwards=false
关键词：
Laminar Neural Field Models Visual

项目摘要

This project is focused on developing improved insights into the functioning of the primary visual cortex of the brain. This is the first region of the cerebral cortex (the convoluted part of the brain responsible for higher cognitive function in primates) to receive and process visual information from the eyes. It can be viewed as a two-dimensional sheet of millions of brain cells (neurons) communicating with each other via electrical signals. The electrical activity patterns of these neurons encode information about a visual image, which is then processed by other regions of the brain, resulting in the visual perception of a dynamically changing three-dimensional world. Visual information is often represented by spatially structured or coherent activity patterns. Understanding the mechanisms that underpin the origin and maintenance of these dynamical patterns is not only important for understanding the normal functioning of the visual brain, but also the occurrence of pathological states during epileptic seizures and migraines. One of the major challenges in neuroscience is determining how the wiring of the visual brain contributes to the generation of cortical activity patterns. The investigator has developed mathematical models of the primary visual cortex based on models that describe the generation and spread of electrical activity across the two-dimensional cortical sheet. Recent experimental studies indicate, however, that the laminar or layered structure of the primary visual cortex plays a crucial role in the production of these activity patterns. This research project, which is part of a larger collaborative program with the Moran Eye Center at the University of Utah, aims to extend previous mathematical models in order to take into account the laminar structure and determine how it affects a range of spontaneous visual phenomena. The main focus of the collaboration is to use a combination of neurophysiology, anatomy, and computational modeling to understand the functional architecture of the primary visual cortex and its role in visual processing. The Moran group is currently developing the use of light to control genetically modified cells and virus labeling techniques in order to understand the fine-structure of the visual cortex, which will be used to refine the mathematical models. The underlying idea linking the two projects is that the neural circuits used in the mathematical models to understand spontaneous activity are the same as those used to explain observations of the normal response of the cortex to visual stimulations. This project promotes scientific progress in the interdisciplinary field of mathematical neuroscience and vision and contributes to the interdisciplinary training of graduate students and postdocs.The modeling of the primary visual cortex involves the construction and analysis of continuum neural field models, in which the large-scale dynamics of spatially structured networks of neurons is described in terms of nonlinear, integro-differential equations. A major advantage of working with neural fields is that powerful methods from the mathematical theory of nonlinear partial differential equations can be adapted to analyze such models. Almost all previous studies of neural fields have ignored the fact that the cerebral cortex has a laminar structure, with neurons in distinct layers often having distinct stimulus response properties and participating in distinct circuits. There is also extensive coupling between layers via so-called vertical connections. In this project the laminar neural field models will be used study two important examples of spontaneous visual phenomena, namely, binocular rivalry waves and visual hallucinations. One possible mechanism for the latter is based on the idea that some chemical or physical disturbance can destabilize the visual part of the brain, inducing a spontaneous pattern of cortical activity. The geometry of the resulting hallucination thus reflects the intrinsic architecture and symmetry of the visual cortex. Analyzing such patterns can provide further insight in how the brain processes images in normal vision. Binocular rivalry is the phenomenon where perception switches back and forth between different images presented to the two eyes. The resulting fluctuations in perceptual dominance and suppression provide a basis for non-invasive studies of the human visual system and the identification of possible neural mechanisms underlying conscious visual awareness.

这个项目的重点是开发对大脑初级视觉皮质功能的改进见解。这是大脑皮层(负责灵长类动物较高认知功能的大脑的曲折部分)接收和处理来自眼睛的视觉信息的第一个区域。它可以被视为数百万个脑细胞(神经元)通过电信号相互通信的二维片。这些神经元的电活动模式编码了关于视觉图像的信息，然后由大脑的其他区域进行处理，导致对动态变化的三维世界的视觉感知。视觉信息通常由空间结构或连贯的活动模式来表示。了解这些动态模式的起源和维持的机制不仅对了解视觉大脑的正常功能很重要，而且对于癫痫发作和偏头痛期间病理状态的发生也很重要。神经科学中的主要挑战之一是确定视觉大脑的连接如何有助于皮质活动模式的产生。这位研究人员基于描述电活动在二维大脑皮层上的产生和传播的模型，开发了初级视觉皮质的数学模型。然而，最近的实验研究表明，初级视觉皮质的层状或层状结构在这些活动模式的产生中起着至关重要的作用。这项研究项目是与犹他大学莫兰眼睛中心的一个更大的合作项目的一部分，旨在扩展以前的数学模型，以便考虑层流结构并确定它如何影响一系列自发的视觉现象。合作的主要焦点是使用神经生理学、解剖学和计算建模的组合来了解初级视觉皮质的功能结构及其在视觉处理中的作用。莫兰团队目前正在开发利用光来控制转基因细胞和病毒标记技术，以了解视觉皮质的精细结构，这将被用来完善数学模型。将这两个项目联系在一起的基本想法是，数学模型中用来理解自发活动的神经回路与用来解释观察到的大脑皮质对视觉刺激的正常反应的神经回路是相同的。该项目促进了数学神经科学和视觉交叉学科领域的科学进步，并为研究生和博士后的跨学科培养做出了贡献。初级视觉皮质的建模涉及连续统神经场模型的构建和分析，其中神经元空间结构网络的大尺度动力学用非线性积分-微分方程组来描述。与神经场合作的一个主要优势是，可以采用来自非线性偏微分方程组数学理论的强大方法来分析此类模型。几乎所有以前对神经场的研究都忽略了这样一个事实，即大脑皮层具有层状结构，不同层中的神经元往往具有不同的刺激反应特性，并参与不同的回路。通过所谓的垂直连接，各层之间也存在广泛的耦合。在这个项目中，层流神经场模型将被用来研究自发视觉现象的两个重要例子，即双眼竞争波和视觉幻觉。后者的一种可能机制是基于这样一种观点，即某些化学或物理干扰可以破坏大脑视觉部分的稳定，从而导致皮质活动的自发模式。因此，由此产生的幻觉的几何形状反映了视觉皮质的内在结构和对称性。分析这些模式可以进一步洞察大脑如何在正常视觉下处理图像。双目竞争是一种感觉在呈现给两只眼睛的不同图像之间来回切换的现象。由此产生的知觉优势和抑制的波动为人类视觉系统的非侵入性研究和识别潜在的有意识视觉意识的可能神经机制提供了基础。