Spatiotemporal patterns of neural activity and their role in perception

项目概要/摘要 动物或人类感知微妙环境刺激的能力不是一个固定的参数。 相反，感知阈值随着唤醒、注意力和期望的变化而波动。同样，个人 当重复呈现相同的刺激时，视觉皮层内的神经元表现出不同的反应， 被广泛认为有助于感知可变性的观察。然而，注入相同的噪声电流， 引起高度精确的尖峰序列，表明这些波动不是由于嘈杂的尖峰机制。 相反，它在很大程度上反映了来自皮层网络的每时每刻的突触输入。这些网络 波动反映在局部场电位（LFP）中。在这里，新的计算方法已经被用来 第一次表明自发波动在清醒时被组织成行波， 灵长类动物这些方法使行波跟踪的每时每刻的基础上，没有试验 平均分析。自发波产生升高和抑制尖峰活动的周期， 初步数据表明，它们调节刺激诱发的尖峰反应和感知灵敏度， 视觉检测任务。因此，集合的团队很好地理解了新皮层的作用， 感知中的行波，并提出三个目标。目的1：测试清醒时是否有自发活动 绒猴MT和V1被组织成行波。犹他州阵列将植入绒猴区MT 和V1来记录尖峰和LFP，而猴子注视空白屏幕。网络波动将是 检测到测试的假设，自发尖峰活动产生行波，可以是 在LFP中检测到，并且LFP波动的相位反映了去极化和超极化的周期。 目的2：建立一个连接LFP波、发放活动和感知的发放网络模型。初步 计算模型已经开发，占穗LFP的关系观察到的实验 数据在这里，该模型将被扩展到定量匹配所观察到的行波的属性， 然后用于生成关于LFP波的相位如何影响尖峰概率的可测试预测， 刺激诱发的反应，和感知灵敏度（后者通过在理想的范围内扩展模型）。 观察员框架）。目的3：确定行波对感官知觉的影响。模型 预测，自发行波将增加和减少刺激诱发的增益 响应，取决于波的相位。为了测试这一点，将在MT和V1内记录自发波， 绒猴试图探测微弱的视觉刺激。这也将使研究人员能够测试模型预测 波的相位调节感知灵敏度总之，这些分析将有助于描述 自发行波对皮层变异性和感知的贡献， 了解与感知和注意力缺陷相关的大脑疾病，如自闭症， 精神分裂症和老年痴呆症

Project Summary/Abstract The ability for an animal or human to perceive a subtle environmental stimulus is not a fixed parameter. Rather, perceptual thresholds fluctuate with changes in arousal, attention, and expectation. Similarly, individual neurons within the visual cortex exhibit variable responses when repeatedly presented the same stimulus, an observation widely thought to contribute to perceptual variability. However, injection of identical noisy currents evokes highly precise spike trains, indicating that these fluctuations are not due to a noisy spiking mechanism. Instead, it largely reflects moment-by-moment synaptic input from the cortical network. These network fluctuations are reflected in local field potentials (LFPs). Here, new computational methods have been used to show for the first time that spontaneous fluctuations are organized into traveling waves in awake, behaving primates. These methods enable tracking of traveling waves on a moment-by-moment basis, without trial averaging analyses. Spontaneous waves create periods of both elevated and suppressed spiking activity, and preliminary data indicate that they modulate stimulus-evoked spiking responses and perceptual sensitivity in a visual detection task. Thus, the assembled team is well positioned to understand the role of neocortical traveling waves in perception and propose three Aims. Aim 1: Test whether spontaneous activity in awake marmoset MT and V1 is organized into traveling waves. Utah arrays will be implanted in marmoset area MT and V1 to record spikes and LFPs while the monkey fixates a blank screen. Network fluctuations will be detected to test the hypothesis that spontaneous spiking activity generates traveling waves that can be detected in the LFP, and the phase of LFP fluctuations reflect periods of depolarization and hyperpolarization. Aim 2: Develop a spiking network model linking LFP waves, spiking activity and perception. A preliminary computational model has been developed that accounts for spike-LFP relationships observed in experimental data. Here, the model will be extended to quantitatively match properties of observed traveling waves, and then used to generate testable predictions about how the phase of LFP waves affects spiking probability, stimulus-evoked responses, and perceptual sensitivity (the latter by extending the model within an ideal observer framework). Aim 3: Determine the impact of traveling waves on sensory perception. The model predicts that spontaneous traveling waves will both increase and decrease the gain of a stimulus-evoked response, depending on wave phase. To test this, spontaneous waves will be recorded within MT and V1 as marmosets attempt to detect a faint visual stimulus. This will also allow researchers to test the model prediction that wave phase regulates perceptual sensitivity. Together, these analyses will help characterize the contributions of spontaneous traveling waves to cortical variability and perception, information critical for understanding brain disorders associated with failures in perception and attention, such as autism, schizophrenia, and Alzheimer’s disease.