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

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， 共同努力对感官刺激进行编码，并将感官感知转化为适当的行为选择。 我们关于感知决策的知识中的一个根本差距是理解连接是如何 皮层微电路塑造了神经元群体中的动力学和信息代码。这一差距已经出现 因为解剖学的连通性和活动性通常是分开研究的，而且因为一个 缺乏了解大脑皮层微电路结构-功能关系的计算框架。这里, 我们将组建一支技能互补的研究团队来解决这个问题。我们将联合起来 利用双光子钙成像研究种群编码和动力学的方法 小鼠的复杂决策任务，用电子测量成像神经元的连通性 基于显微镜(EM)的连接学。此外，我们将使用我们的活动和连接数据来开发 数据驱动模型，用于探索大脑皮层微电路之间的结构-功能关系。 我们将应用我们的新方法来研究种群编码、微电路连接和结构- 不同大脑皮层的功能关系不同，在知觉决策期间执行不同的计算任务- 制作。尽管感觉皮质和联想皮质执行不同的功能是众所周知的，但很少有 了解这些不同角色的潜在机制，包括微电路连接方面的差异 和种群编码方案。在第一个目标中，我们将比较人口编码和微电路连接 用于视觉皮质(V1；感觉皮质)和后顶叶皮质的感觉刺激和行为选择 (PPC；联想皮层)。我们将使用计算工具来检查不同的编码方案如何提供 功能优势。我们将在V1和PPC中使用EM连接学来处理在感知过程中成像的神经元 探索刺激和选择代码的结构-功能关系的决策任务。我们将开发一个数据- 驱动递归神经网络模型，将连通性和种群活动联系起来。在第二个目标中，我们将 研究神经元群体如何使用微电路将感觉信息转换为行为选择 连通性。我们将开发一个新的统计概念-交集信息-来识别活动模式 V1和PPC携带感官信息，告知行为选择。使用EM Connectomics，我们将 重建细胞之间的微电路连接，以测试关于感觉到选择信息的假设 流。我们的工作将是比较大脑皮质种群编码和微电路连接的第一批工作之一 并探索感性决策的结构-功能关系。

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.