Rival Networks: Dissecting the Canonical Circuit of Bi-stable Visual Perception

竞争网络：剖析双稳态视觉感知的规范电路

• 批准号: 10248298
• 负责人: Ganna Palagina
• 金额: $ 21.28万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248298
• 关键词: Anatomy; Area; Brain; Cells; Computer Models; Cortical Column; Data; Dissection; Electrophysiology (science); Etiology; Evolution; Functional Magnetic Resonance Imaging; Future; Human; Image; Individual; Interneurons; Light; Maintenance; Maps; Mechanics; Mediating; Modeling; Monitor; Motion; Motor Cortex; Mus; Neurons; Neurosciences; Noise; Pathologic Nystagmus; Pattern; Perception; Population; Prefrontal Cortex; Primates; Principal Investigator; Process; Property; Psychophysics; Pyramidal Cells; Reporting; Resolution; Role; Sensory; Shapes; Stimulus; Structure; Study models; Testing; Texture; Time; Transgenic Mice; Visual; Visual Perception; Work; area V1; area V2; base; behavioral study; cortex mapping; expectation; experimental study; extrastriate visual cortex; follow-up; hippocampal pyramidal neuron; in vivo; mouse model; neuronal circuitry; optogenetics; rapid eye movement; relating to nervous system; response; study population; tool; two photon microscopy; two-photon; visual motor; visual stimulus; wide area network

Principal Investigator (Last, First, Middle): Palagina, Ganna Abstract / Summary Rival Networks: Dissecting the Canonical Circuit of Bi-stable Visual Perception Viewing visual stimuli with several mutually exclusive interpretations causes subjective perception to vacillate between the interpretations. This process is known as multi-stable perception and provides an excellent well- controlled model for studying how the percepts are formed and maintained in the brain. Multiple hypotheses are proposed for the circuit mechanisms of multi-stable perception: changes in neuronal synchrony, adaptation of population firing rates, mutual inhibition between rival neuronal populations, neural noise and hierarchical inference across the network of cortical areas and subcortical structures. Supporting evidence for involvement of these processes comes from computational models, psychophysical studies, fMRI and TMS studies in humans and single-unit primate electrophysiology. These approaches established that multi-stable perception is a distributed process involving the cooperative network of both low-level and high- level cortical areas. They also made it clear that the activity of single units in the brain cannot be used as a clear indicator of the rivaling percepts, and that one must look at the level of neuronal circuit to understand the process. However, until recently, studying population responses and interplay between the circuits in different cortical areas at the single-cell resolution was a limited possibility. Currently, there is no mechanistic understanding of canonical cortical computations that underlie perceptual transitions at the level of cortical column and local sub-networks. Moreover, there is no circuit-level single-cell data on the interactions between the circuits across the cortical area borders during perceptual rivalry. Recent advances enable us for the first time to map at single cell resolution the dynamics of columnar sub-networks during bi-stable perception. We will do this in in primary sensory area (V1) that is required for percept alternations. To study the behavioral contribution of circuit components and test for causality we will use optogenetic control of specific cell populations with SLM (aim #1). We will then follow up by whole- hemisphere imaging to identify higher-order areas that are the part of multi-stable perception functional network. We will do multiple-area imaging of layer 2/3 sub-circuits in V1, V2 and VRL/A/ALto get a handle on long-range interactions between circuit components and evolution of percept and reversal encoding in primary sensory area V1 and higher-order areas up in the cortical hierarchy during bi-stable perception (aim #2). Expectations: 1) Identify the “bi-stable perception network” of the mouse brain, composed of areas putatively involved in percept stability and reversal 2) Obtain the first comprehensive picture of the dynamics of circuit interactions between sub-networks of pyramidal cells and interneurons in cortical columns during bi-stable perception, across V1, LGN inputs to V1, V2 and visuomotor area VRL/A/AL 3) Identify the contribution of specific processes to perceptual reversals: firing rates adaptation, mutual inhibition of rival sub-networks, synchrony and variability of firing.

主要研究者（末次、首次、中间）：Palagina、Ganna 摘要/概要 竞争网络：双稳态视觉感知的典型回路剖析 用几种相互排斥的解释来观察视觉刺激会导致主观感知摇摆不定 之间的解释。这个过程被称为多稳定感知，并提供了一个很好的- 控制模型，用于研究感知如何在大脑中形成和维持。多个假设 提出的电路机制的多稳定的看法：神经元同步的变化，适应 群体放电率，竞争神经元群体之间的相互抑制，神经噪声和层次推理 穿过皮层区域和皮层下结构的网络。 支持参与这些过程的证据来自计算模型，心理物理模型， 研究，功能磁共振成像和TMS研究在人类和单一单位灵长类动物电生理学。这些方法建立了 多稳态感知是一个分布式过程，涉及低级和高级的合作网络， 水平皮层区域。他们还明确指出，大脑中单个单位的活动不能作为一个明确的指标。 这是竞争感知的指标，人们必须观察神经元回路的水平才能理解这一过程。 然而，直到最近，研究群体反应和不同皮层区域回路之间的相互作用， 在单细胞分辨率下是有限的可能性。目前，对规范还没有机械的理解 皮层计算是皮层列和局部子网络水平上感知转换的基础。 此外，还没有关于跨皮层区域的回路之间的相互作用的回路水平的单细胞数据 在知觉竞争中的边界。最近的进展使我们能够第一次以单细胞分辨率绘制 双稳态感知过程中柱状子网络的动力学。我们将在初级感觉区（V1）中这样做， 需要进行修改。研究电路元件的行为贡献和因果关系检验 我们将使用SLM对特定细胞群进行光遗传学控制（目的#1）。我们将全面跟进- 半球成像以识别作为多稳定感知功能网络的一部分的高阶区域。 我们将对V1、V2和VRL/A/AL中的第2/3层子电路进行多区域成像，以处理远程 初级感觉V1区回路成分与感受器和反转编码进化的相互作用 以及在双稳态感知期间皮层层级中的高阶区域（目标#2）。 期望：1）识别小鼠大脑的“双稳态感知网络”，由脑区组成 2）获得电路动力学的第一个全面的图像 双稳态过程中锥体细胞和皮质柱中间神经元子网络之间的相互作用 感知，跨V1，LGN输入到V1，V2和视觉区域VRL/A/AL 3）识别特定的 知觉逆转的过程：放电率适应，竞争对手子网络的相互抑制，同步性和 射击的可变性。