Spatiotemporal patterns of neural activity and their role in perception

项目总结 当重复呈现相同的信息时，视觉皮质中的单个神经元表现出不同的反应 刺激，这些波动预测是否会检测到微弱的目标。在前一轮融资中， 该研究小组发现，在清醒的大脑MT区自发地产生“内在行波”(iTW 一只绒猴。以前的研究已经观察到了iTWs，但由于动物被麻醉，它仍然未知 (1)如果iTW发生在清醒的动物身上，以及(2)iTW在知觉中扮演什么角色(如果有的话)。研究小组发现，iTW 创造高涨和抑制尖峰活动的周期。他们还发现，iTW联合调制 视觉检测任务中刺激诱发的尖峰反应和知觉敏感度。一场大规模的 该团队开发的尖峰网络模型预测，iTW是轴突时间延迟的结果 沿着视觉区域常见的长距离水平纤维传导。这些结果增加了一种可能性 ITW是一种新的、动态的增益控制机制。在这场竞争性的更新中，这项实验--以及 理论驱动的团队处于有利地位，可以通过揭示iTW的 与视觉系统中特定连接模式的连接。这项工作将在三个具体方面取得进展 目标。目标1：测试iTW分为不同的模体来调节神经反应增益和 以一种特征选择的方式感知。该团队的网络模型预测，集群、特定功能 MT区皮质柱之间的连接将产生穿越相似调谐神经元的子网络的iTW。 他们将在MT地区的记录中测试这一理论驱动的预测。目标2：检验iTW变化的预测 它们在大脑皮层各层之间的影响和时间。使用层流和ECoG组合阵列 ，他们将研究iTW对区域的颗粒上、颗粒和颗粒下层的影响。 Mt.Mt.目标3：测试对iTW在行为中的作用做出相反预测的竞争性已发表模型。 一类模型预测iTW在正在进行的非驱动活动(即所谓的噪声)中驱动成对的关联 相关性“)，这会损害感官辨别能力。因此，根据这些模型，iTW是 需要减少的信息限制相关性，以改善感觉处理。ITW的模型 相反，该团队预测，iTW对两两的相关性没有明显的贡献 不受驱动的活动，它们通过调节感觉诱发反应的增益来改善感觉处理， 这种效果类似于注意力依赖的增益调节所产生的益处。总而言之， 拟议的实验将阐明iTW背后的神经机制，并探讨它们在两者中的作用。 刺激物的检测和识别。此外，这些实验还将检验关于这一角色的新假说 连接相似调谐神经种群的长距离水平纤维。这个团队将测试这些通路是否， 它们本身是由视觉环境的自然统计数据塑造的，并在这样做时引导ITW 提供了一种有选择地对环境中通常存在的视觉特征进行采样的新方法。

PROJECT SUMMARY Individual neurons within the visual cortex exhibit variable responses when repeatedly presented with the same stimulus, and these fluctuations predict whether or not a faint target will be detected. In the prior round of funding, this team discovered that “intrinsic traveling waves” (iTWs) occur spontaneously in visual area MT of the awake marmoset. Previous studies had observed iTWs, but since the animals were anesthetized, it remained unknown (1) if iTWs occur in awake animals and (2) what role, if any, iTWs play in perception. The team found that iTWs create periods of both elevated and suppressed spiking activity. They also found that iTWs conjointly modulate both stimulus-evoked spiking responses and perceptual sensitivity in a visual detection task. A large-scale spiking network model developed by the team predicts that iTWs are a result of the time delays from axonal conduction along the long-range horizontal fibers common across visual areas. These results raise the possibility that iTWs are a new, dynamic mechanism of gain control. In this competitive renewal, this experiment- and theory-driven team is well positioned to conduct experiments to test this role for iTWs by uncovering their connection to specific patterns of connectivity in the visual system. This work will progress in three Specific Aims. Aim 1: Test the hypothesis that iTWs fall into distinct motifs that regulate neural response gain and perception in a feature-selective manner. The team’s network model predicts that clustered, feature-specific connectivity among cortical columns in area MT will create iTWs that traverse subnetworks of like-tuned neurons. They will test this theory-driven prediction in recordings from Area MT. Aim 2: Test the prediction that iTWs vary in their impact and timing across layers of the cortical column. Using combined laminar and ECoG array recordings, they will examine the impact of iTWs in the supragranular, granular, and infragranular layers of area MT. Aim 3: Test competing published models of iTWs that make opposite predictions about their role in behavior. One class of models predicts that iTWs drive pairwise correlations in ongoing undriven activity (so-called “noise correlations”), which impair sensory discrimination. iTWs are thus, according to these models, a source of information-limiting correlations that need to be diminished to improve sensory processing. The model of iTWs developed by this team predicts, instead, that iTWs make no measurable contribution to pairwise correlations in undriven activity and that they improve sensory processing by regulating the gain of sensory evoked responses, an effect analogous to the benefits that stem from attention-dependent regulation of gain. Taken together, the proposed experiments will elucidate the neural mechanisms underlying iTWs and probe their roles in both detection and discrimination of stimuli. In addition, these experiments will test a new hypothesis about the role of long-range horizontal fibers that link similarly tuned neural populations. This team will test if these pathways, which are themselves sculpted by the natural statistics of the visual environment, guide iTWs and in so doing provide a novel means of selectively sampling visual features that are typically found in the environment.