Discovering brain state dependent dynamics in large scale perceptual ensembles.

项目摘要 主动知觉是一种寻找行为相关信息的能力，由认知和运动共同引导 行为，并受到内源性大脑状态波动的影响。这是一项共同努力的结果 感觉层次中的神经元集合。这些乐团灵活而动态地互动，因为 有机体在各种行为状态和大脑状态之间的转换。然而，依赖于状态的信息 支撑这类合奏活动的加工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，在一个集合中，依赖关系，如协变性，强烈地 影响它们的信息承载能力，从而影响它们的功能效能。进一步，理论研究。 揭示了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-一直是 被确定为这一路线的关键决定因素。确定这一来源的关键一步是确定 这些群体中群体的联合尖峰活动(超越成对相关)。然而，这是一种 由于神经元相互作用的各种非线性性质，以及神经细胞 目前的记录技术对种群进行了稀疏的采样。解决这个问题需要高度的 跨学科方法横跨系统和计算神经科学的先进技术。基座 根据我们的初步数据和之前的研究，我们的广泛假设是，主动知觉的计算是 皮质层特异性的，并且它们是由由其层定义的神经亚群的集合来调节的 身份和细胞类别。我们建议回答关于这一假设的几个关键问题：Active是如何 知觉调节层流回路中的信息流，在注意力(目标1A)和眼跳时都是如此 运动(目标1B)？主动知觉的层状回路是如何受大脑内部状态调节的 像线索注意(目标2A)和自发视觉(目标2B)期间的波动？我们将实现 这些目的是使用非人类灵长类动物的视觉皮质中的层流高密度记录，而动物则是 从事基于任务的或自发的主动知觉。使用一种新的动态贝叶斯组合 网络 其中 (DBN)和部分信息分解(PID)，我们将推断出不同类别的依赖关系 大脑皮层网络组件。我们的建议将实现调制的第一个系统表征 在层流皮层背景下，通过主动知觉过程的信息流(超越成对关联) 电路。我们的建议是第一个研究大脑内部状态波动如何塑造整体的研究 主动知觉的水平因果主题。这些调查的结果将大大推进我们的 理解正则皮层回路中的信息流结构，并提供一个广泛的框架 研究全脑回路中的这种结构。

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.