权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10460526
负责人：
Christopher D Harvey
金额：
$ 113.53万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10460526
关键词：
Address Alzheimer&apos 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 data-driven model 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

项目摘要

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.

项目摘要在知觉决策过程中，排列在高度互连的微电路中的神经元群体，共同编码感官刺激，并将感官知觉转化为适当的行为选择。我们对知觉决策的认识中的一个根本差距是理解皮质微电路在神经元群体中塑造动力学和信息代码。这一差距出现了因为解剖学上的连通性和活动性通常是分开研究的，计算框架来理解皮层微电路中的结构-功能关系是缺失的。在这里，我们将组建一个具有互补技能的研究人员团队来解决这个问题。我们将联合收割机在一个新的和小鼠的复杂决策任务，使用电子显微镜测量成像神经元的连接性。基于显微镜（EM）的连接组学。此外，我们将使用我们的活动和连接数据来开发一个数据驱动的模型，以探索结构功能之间的关系，在皮层微电路。我们将应用我们的新方法来研究人口代码，微电路连接和结构- 在知觉决策过程中，大脑皮层的功能关系不同，以执行不同的计算任务，制作。虽然已经确定感觉皮层和联合皮层执行不同的功能，了解这些不同作用的机制，包括微电路连接的区别和人口编码方案。在第一个目标，我们将比较人口代码和微电路连接视觉皮层（V1;感觉皮层）和后顶叶皮层的感觉刺激和行为选择 (PPC;联合皮质）。我们将使用计算工具来研究不同的编码方案如何提供功能效益。我们将在V1和PPC中使用EM连接组学，用于感知期间成像的神经元。决策任务，以探测刺激和选择代码的结构-功能关系。我们会建立一个数据库- 驱动的递归神经网络模型，将连通性和种群活动联系起来。第二个目标，我们将研究神经元群体如何使用微电路将感觉信息转化为行为选择连通性。我们将开发一个新的统计概念-交叉口信息-以确定活动模式， V1和PPC携带的感官信息，告知行为选择。使用EM连接组学，我们将重建细胞之间的微电路连接，以测试关于感官选择信息的假设流我们的工作将是第一个比较群体编码和大脑皮层微电路连接的工作。区域和探索结构功能关系的知觉决策。