Studying perceptual decision-making across cortex by combining population imaging, connectomics, and computational modeling

通过结合群体成像、连接组学和计算模型来研究跨皮层的感知决策

• 批准号: 10242172
• 负责人: Christopher D Harvey
• 金额: $ 114.36万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242172
• 关键词: Address; Alzheimer' s Disease; Anatomy; Behavioral; Bipolar Disorder; Calcium; Cells; Code; Communities; Complex; Computer Models; Coupling; Cues; Data; Data Set; Decision Making; Electron Microscopy; Esthesia; Image; Knowledge; Measurement; Measures; Methods; Modeling; Mus; Neural Network Simulation; Neurons; Neurophysiology - biologic function; Output; Parietal Lobe; Pattern; Perception; Play; Population; Process; Recurrence; Research Personnel; Resources; Role; Sampling; Scheme; Schizophrenia; Sensory; Shapes; Stimulus; Structure-Activity Relationship; Testing; Time; Visual Cortex; Work; association cortex; autism spectrum disorder; base; computer framework; computerized tools; connectome data; nervous system disorder; network models; neural circuit; neuropsychiatric disorder; novel; novel strategies; reconstruction; recurrent neural network; relating to nervous system; sensory cortex; sensory stimulus; skills; study population; synergism; tool; two-photon; virtual reality; visual coding

Project Summary During perceptual decision-making, populations of neurons, arranged in highly interconnected microcircuits, work together to encode sensory stimuli and to transform sensory perception into appropriate behavioral choices. A fundamental gap in our knowledge about perceptual decision-making is understanding how the connectivity in cortical microcircuits shapes dynamics and information codes in populations of neurons. This gap has arisen because anatomical connectivity and activity have generally been studied separately, and because a computational framework to understand structure-function relationships in cortical microcircuits is missing. Here, we will assemble a team of researchers with complementary skills to tackle this problem. We will combine approaches to study population coding and dynamics using two-photon calcium imaging during a novel and complex decision task for mice, with measurements of connectivity in the imaged neurons using electron microscopy (EM)-based connectomics. Furthermore, we will use our activity and connectivity data to develop a data-driven model to explore structure-function relationships across cortical microcircuits. We will apply our new approach to investigate how population codes, microcircuit connectivity, and structure- function relationships differ across cortex to perform distinct computational tasks during perceptual decision- making. Although it is well established that sensory and association cortices perform different functions, little is known about the mechanisms underlying these different roles, including distinctions in microcircuit connectivity and population coding schemes. In a first aim, we will compare population codes and microcircuit connectivity for sensory stimuli and behavioral choices in visual cortex (V1; sensory cortex) and posterior parietal cortex (PPC; association cortex). We will use computational tools to examine how distinct coding schemes provide functional benefits. We will use EM connectomics in V1 and PPC for neurons imaged during a perceptual decision task to probe structure-function relationships for stimulus and choice codes. We will develop a data- driven recurrent neural network model to relate connectivity and population activity. In a second aim, we will investigate how neuronal populations transform sensory information into behavioral choices using microcircuit connectivity. We will develop a new statistical concept – intersection information – to identify activity patterns in V1 and PPC that carry sensory information that informs behavioral choices. Using EM connectomics, we will reconstruct the microcircuit connectivity between cells to test hypotheses about sensory-to-choice information flow. Our work will be some of the first to compare population coding and microcircuit connectivity across cortical regions and to explore structure-function relationships for perceptual decision-making.

项目摘要 在感知决策过程中，在高度相互连接的微电路中排列的神经元的poptions， 共同努力以编码感觉刺激并将感觉易感转变为适当的选择。 我们关于感知决策的基本差距是了解联系 皮质微电路形状的神经元种群中的动态和信息代码。 因为解剖学连接和活动通常已经分别研究了，因为 缺少了解皮质微电路中结构 - 功能的计算框架。 我们将组建一个具有互补技能的研究人员，以解决这个问题 在小说和 小鼠的复杂决策任务，使用电子中成像神经元的连通性测量 基于显微镜（EM）的连接组。 数据驱动的模型，以探索跨皮质微核的结构 - 功能。 我们将采用新方法来研究人口代码，微电路连接和结构如何 在感知决策过程中，功能关系在皮质之间不同，以执行不同的计算任务 - 制作。 知道不同角色的基础机制，包括微电路连接中的区别 和POPTION编码方案。 用于视觉皮层（V1；感觉皮层）和后顶叶皮层中的感觉刺激和行为选择 （PPC；协会皮质）。 功能好处。 探测刺激和选择代码的结构功能的任务。 驱动的复发性神经网络模型以第二个目标关联连通性和人口活动。 研究神经元的Poptions如何使用微电路将感觉信息转化为行为选择 连接性。我们将开发一个新的统计概念 - 交集信息 V1和PPC携带感官信息，可为行为选择提供信息。 重建细胞之间的微电路连通性，以测试有关选择信息的假设 流程。 区域并探索用于感知决策的结构功能。