Traveling waves in neocortical circuits: Mechanisms, computational roles in sensory processing, and impact on sensory perception

项目总结/摘要 神经科学研究的一个重要的长期目标是了解大规模时空 大脑中出现的神经活动模式，以及它们是否在塑造大脑的 计算过程和哺乳动物的行为。值得注意的是，神经活动的行波 与感觉、认知和运动行为有关，这个团队最近发现， 视觉皮层区的iTW预测了诱发的尖峰活动和视觉感知的大小。 清醒的非人类灵长类动物的敏感性。然而，目前尚不清楚iTW是否反映了 皮质功能的潜在基本机制，或者它们是否只是副现象。 这个项目将研究新皮层iTW在视觉感知中的作用，并以理论模型为指导 iTW活动，使具体的可测试和可证伪的预测的机制，引起了 的时空特征及其在感觉行为中的作用。该模型是基于一个网络的尖峰 神经元，并预测iTW从水平皮质纤维的解剖组织中出现， 在波动的兴奋性（E）和抑制性（I）群体活动平衡的变化中， 进一步预测iTW在视觉皮层区域的视网膜定位协调，并且这种协调 是iTW改善感知的机制。然而，这些预测可能是错误的。实证 对模型预测的测试将需要对特定神经元类型的活动进行测量，跨越广泛的领域- 的视图，并具有良好的空间和时间分辨率清醒的行为动物。 为了进行这样的测量，该团队最近发明了几种新技术：（1）基因编码 荧光电压指示器（GEVI），它们将针对特定的E或I神经群体;（2）定制的 双色荧光显微镜跟踪2种神经元类型的阈下群体电压动力学， 一次，跨越绒猴和小鼠皮质中多个皮质区域的8 mm视野;（3） 透明电极阵列，允许同时跨皮层执行LFP测量 用电压成像研究E和I群体活动;（4）透明层状电极阵列， 不同皮层细胞的尖峰活动。通过结合使用这四项创新， 研究小组将对绒猴和老鼠进行视觉检测任务， 并了解不同的皮层神经元类型如何影响iTW动力学和感知灵敏度。 目的1测试E/I群体活动在小鼠和狨猴中作为iTW传播的假设。目标2测试 预测iTW在皮层区域之间的视网膜定位协调，并且这种协调增强了 知觉灵敏度目标3将从理论上解释目标1和目标2的结果， 接地尖峰网络模型总的来说，通过使用创新的新技术，这个跨学科的团队将 提供了对大脑研究具有深远意义的长期概念问题的关键见解。

PROJECT SUMMARY/ABSTRACT An important longstanding goal in neuroscience research is to understand how large-scale spatiotemporal patterns of neural activity emerge in the brain and whether they have a direct role in shaping the brain’s computational processes and thereby mammalian behavior. Notably, traveling waves of neural activity have been implicated in sensory, cognitive, and motor behaviors, and this team recently found that intrinsic traveling waves (iTWs) in visual cortical areas predict the magnitude of evoked spiking activity and visual perceptual sensitivity in awake, behaving non-human primates. However, it remains unclear whether iTWs reflect an underlying fundamental mechanism of cortical function or whether they might simply be epiphenomenal. This project will examine the role of neocortical iTWs in visual perception and is guided by a theoretical model of iTW activity that makes specific testable and falsifiable predictions about the mechanisms that give rise to the spatiotemporal features of iTWs and their roles in sensory behavior. The model is based on a network of spiking neurons and predicts that iTWs emerge from the anatomical organization of horizontal cortical fibers and result in shifts in the balance of excitatory (E) and inhibitory (I) population activity that travel as waves, The model further predicts that iTWs are retinotopically coordinated across visual cortical areas, and that this coordination is the mechanism by which iTWs improve perception. However, these predictions could be wrong. Empirical tests of the model’s predictions will require activity measurements from specific neuron-types, across wide fields- of-view, and with excellent spatial and temporal resolution in awake behaving animals. To perform such measurements, the team recently invented several new technologies: (1) genetically encoded fluorescent voltage indicators (GEVIs) that they will target to specific E or I neural populations; (2) a custom-built dual-color fluorescence mesoscope to track the subthreshold population voltage dynamics of 2 neuron-types at once, across a 8-mm field-of-view spanning multiple cortical areas in marmoset and mouse cortex; (3) transparent electrode arrays that allow measurements of LFPs to be performed across the cortex concurrently with voltage imaging studies of E and I population activity; (4) transparent laminar electrode arrays to measure the spiking activity of cells across the different cortical layers. By combining the use of these four innovations in marmosets and mice performing a visual detection task, the team will test the predictions of the theoretical model and learn how different cortical neuron-types impact iTW dynamics and perceptual sensitivity. Aim 1 tests the hypothesis that E/I population activity travels as iTWs in mice and marmosets. Aim 2 tests the prediction that iTWs are coordinated retinotopically across cortical areas, and that this coordination enhances perceptual sensitivity. Aim 3 will account mechanistically for the results of Aims 1 and 2 in a theoretically grounded spiking network model. Overall, by using innovative new technologies this interdisciplinary team will provide key insights into longstanding conceptual issues of profound importance to brain research.