Discovering brain state dependent dynamics in large scale perceptual ensembles.

在大规模感知集合中发现大脑状态依赖的动力学。

• 批准号: 10568047
• 负责人: Monika P. Jadi
• 金额: $ 50.56万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10568047
• 关键词: Animals; Arousal; Attention; Attention Deficit Disorder; Bayesian Network; Behavior; Behavioral; Brain; Carrying Capacities; Categories; Cells; Code; Cognitive; Computer Models; Cues; Data; Dependence; Dimensions; Drowsiness; Frequencies; Investigation; Joints; Mediating; Memory; Modeling; Nature; Neurons; Organism; Output; Perception; Performance; Population; Process; Saccades; Sampling; Schizophrenia; Sensory; Shapes; Source; Statistical Data Interpretation; Structure; System; TBI Patients; Techniques; Testing; Time; Vision; Visual Cortex; Work; area V4; autism spectrum disorder; awake; computational neuroscience; density; extrastriate visual cortex; flexibility; information processing; interdisciplinary approach; motor behavior; neural; neural correlate; neuropathology; neurophysiology; nonhuman primate; novel; oculomotor; selective attention

Project Summary Active perception, the ability to seek out behaviorally relevant information, is guided by both cognitive and motor behaviors and is influenced by fluctuations in endogenous brain state. It is a result of the concerted activity of ensembles of neurons in the sensory hierarchy. These ensembles interact flexibly and dynamically as the organism transitions between various behavioral and brain states. However, the state-dependent information processing principles that underlie the activity of such ensembles are largely unknown. A rich body of theoretical and recent experimental work has shown that dependencies, such as co-variability, within an ensemble strongly influence their information carrying capacity and hence their functional efficacy. Further, theoretical investigation of these dependencies has revealed that their influence is determined by the extent of their alignment with the information coding dimension of an ensemble. The source of dependencies – shared vs. local – has been identified as a key determinant of this alignment. A critical step toward determining this source is to characterize the joint spiking activity (beyond pairwise correlations) of populations in these ensembles. This is, however, a challenging task owing to the varied non-linear nature of neuronal interactions, and the fact that neural populations are sparsely sampled by current recording techniques. Tackling this problem requires a highly interdisciplinary approach spanning advanced techniques in systems and computational neuroscience. Based on our preliminary data and prior studies, our broad hypothesis is that the computations of active perception are cortical layer-specific and that they are mediated by ensembles of neural sub-populations defined by their layer identity and cell-class. We propose to answer several key questions regarding this hypothesis: how does active perception modulate information flow in laminar circuits, both during attention (Aim 1A) and saccadic eye movement (Aim 1B)? How are the laminar circuits of active perception modulated by internal brain state fluctuations such as those during cued attention (Aim 2A) and spontaneous vision (Aim 2B)? We will achieve these aims using laminar high-density recordings in the visual cortex of non-human primates, while animals are engaged in either task-based or spontaneous active perception. Using a novel combination of dynamic Bayesian networks among (DBN) and partial information decomposition (PID) we will infer distinct categories of dependencies cortical network components. Our proposal will achieve the first systematic characterization of modulation of information flow (beyond pairwise correlations) by active perception processes in the context of laminar cortical circuits. Our proposal is the first study to investigate how internal brain state fluctuations shape the ensemble level causal motifs of active perception. The results of these investigations will significantly advance our understanding of information flow structure in a canonical cortical circuit and provide a broad framework for investigating such structures in brain-wide circuits.

项目摘要 主动知觉是一种寻找行为相关信息的能力，它受认知和运动的双重指导 行为，并受到内源性大脑状态波动的影响。这是一个协调行动的结果， 感觉层级中的神经元集合。这些集合灵活而动态地相互作用， 生物体在各种行为和大脑状态之间转换。然而，状态依赖信息 作为这种集合活动基础的处理原理在很大程度上是未知的。丰富的理论体系 最近的实验工作表明，依赖性，如协变，在一个合奏强烈 影响其信息承载能力，从而影响其功能功效。此外，理论研究 这些依赖关系的研究表明，它们的影响力取决于它们与 集合的信息编码维度。依赖关系的来源-共享与本地-已经被 被认为是这一调整的关键决定因素。确定此来源的关键步骤是描述 联合尖峰活动（超越成对相关性）的人口在这些合奏。然而，这是一个 具有挑战性的任务，由于神经元相互作用的各种非线性性质， 目前的记录技术对人口进行了稀疏抽样。解决这个问题需要一个高度 跨学科的方法跨越系统和计算神经科学的先进技术。基于 根据我们的初步数据和先前的研究，我们的广泛假设是，主动感知的计算是 皮质层特异性的，它们是由它们的层定义的神经亚群的集合介导的 身份和细胞类别。我们建议回答关于这个假设的几个关键问题： 知觉在注意（Aim 1A）和眼跳过程中调节层状回路中的信息流 运动（目标1B）？主动知觉的层状回路是如何被内部脑状态所调制的 波动，如在线索注意（目标2A）和自发视觉（目标2B）？我们将实现 这些目标使用非人类灵长类动物视觉皮层中的层状高密度记录，而动物 参与基于任务的或自发的主动感知。使用动态贝叶斯的新组合 网络 之间 (DBN)和部分信息分解（PID），我们将推断不同类别的依赖关系 皮质网络成分。我们的建议将实现调制的第一个系统的表征 信息流（超越成对相关性）的主动感知过程中的上下文中的层皮质 电路.我们的建议是第一个研究内部大脑状态波动如何塑造合奏 主动感知的层次因果基序。这些调查的结果将大大促进我们的 理解典型皮层回路中的信息流结构，并为以下方面提供广泛的框架： 在全脑回路中研究这种结构。