Topological bridges between circuits, models, and behavior

电路、模型和行为之间的拓扑桥梁

• 批准号: 10208403
• 负责人: Marlene Rochelle Cohen
• 金额: $ 281.4万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10208403
• 关键词: Affect; Area; Attention; Behavior; Behavioral; Biology; Biophysics; Brain; Cognition; Cognitive; Complex; Data; Data Analyses; Decision Making; Diagnosis; Dimensions; Electrophysiology (science); Esthesia; Goals; Image; Information Theory; Interneurons; Link; Macaca mulatta; Mathematics; Measurement; Measures; Metals; Methods; Modeling; Monkeys; Motivation; Mus; Neurons; Oceans; Parietal; Parietal Lobe; Perception; Performance; Physiological; Population; Primates; Process; Psychophysics; Recurrence; Rewards; Role; Sensory; Sensory Process; Stimulus; Structure; System; Testing; Time; Visual; Visual Cortex; Work; algebraic topology; association cortex; cell type; cognitive process; cognitive system; detector; flexibility; information processing; inhibitory neuron; insight; nervous system disorder; neural network; neuronal circuitry; neurophysiology; neuropsychiatric disorder; novel; novel strategies; optogenetics; predictive modeling; relating to nervous system; response; sensory system; success; tool; two-photon

Project summary The plight of the neuroscientist trying to understand the brain using linear analysis methods is akin to studying the makeup of the ocean using the bits you find with a metal detector. Everything we know about the neural basis of decision making, from biology to computation to behavior, makes it clear that the relationship between neurons and behavior is profoundly nonlinear. However, for good mathematical reasons, our attempts to understand that relationship typically rely only on linear measures. These measures have an especially hard time dealing with the reality that neural networks are far from static. Indeed, the flexibility of interactions between neurons, while adding an additional nonlinearity, is a critically important clue about underlying mechanisms and computations. The goal of the proposed project is to test the hypothesis that nonlinear measures of correlated variability in a population of neurons will 1) establish a strong link between neurons and perceptual decisions, 2) constrain models of the circuit mechanisms by which cognition affects perception, and 3) predict the effects of causally manipulating different subtypes of inhibitory interneurons on population activity. We will use and develop methods from algebraic topology to characterize the activity of neuronal populations in a holistic, nonlinear way. Our project leverages the complementary strengths between three highly interactive approaches: primate neurophysiology and psychophysics, modeling neuronal circuits, and two photon imaging and optogenetic manipulation of subtypes of inhibitory interneurons in mice. In Aim 1, we will test the hypothesis that sensory and cognitive processes including contrast, adaptation, attention, and motivation affect performance on visual tasks exactly when they change the topological signatures of the correlated variability in visual or parietal cortex. In Aim 2, we will use a biophysically realistic model to understand which changes in a cortical circuit would or would not change the topological signatures of neuronal population activity. In Aim 3, we will test the predictions of our model to understand how manipulating the activity of different subtypes of inhibitory interneurons affects topological summaries of neuronal activity and information processing in the network. This work uses novel mathematical ideas to bridge different levels of the study of cortical circuits. It will have implications for our understanding of the relationship between neuronal circuits and behavior across species, systems, and theoretical approaches.

项目摘要 试图使用线性分析方法理解大脑的神经科学家的困境类似于研究 使用金属探测器发现的钻头的海洋构成。我们对神经的了解 决策的基础，从生物学到计算再到行为，都清楚地表明 神经元和行为是极端非线性的。但是，出于良好的数学原因，我们的尝试 了解关系通常仅依靠线性措施。这些措施特别困难 处理神经网络远非静态的时间。确实，互动之间的灵活性 神经元在添加额外的非线性的同时，是关于潜在机制和 计算。拟议项目的目的是检验以下假设：非线性相关的度量 神经元人群的变异性会1）在神经元和感知决定之间建立牢固的联系，2） 约束认知影响感知的电路机制的模型，3）预测 因果操纵抑制性中间神经元对种群活动的不同亚型。我们将使用和 开发从代数拓扑的方法，以表征整体中神经元种群的活性 非线性方式。我们的项目利用了三种高度互动方法之间的互补优势： 灵长类神经生理学和心理物理学，建模神经元电路以及两个光子成像和 小鼠抑制性中间神经元亚型的光遗传学操纵。在AIM 1中，我们将检验以下假设 感官和认知过程，包括对比，适应，注意力和动机，影响绩效 当他们更改视觉或顶叶的相关变异性的拓扑特征时，请准确地执行视觉任务 皮质。在AIM 2中，我们将使用一个生物物理现实的模型来了解皮质电路中哪些变化 会或不会改变神经元人口活动的拓扑特征。在AIM 3中，我们将测试 对我们模型的预测，以了解如何操纵抑制性不同亚型的活性 中间神经元会影响网络中神经元活动和信息处理的拓扑总结。这 工作使用新颖的数学思想来弥合皮质回路研究的不同水平。它将有 对我们对神经元电路与跨物种行为之间关系的理解的影响， 系统和理论方法。