Role of fixational eye movements and saccades in processing spatial information in V1-V2-V4 networks

该项目的重点是V1，V2和V4（V1 - 2 - 4）区域的皮质机制，这些区域是信息的基础。 视觉中的扫视和注视眼球运动（FxEM：微扫视和漂移）的处理作用。两 扫视和微扫视之后是漂移。扫视/漂移和微扫视/漂移周期的序列， 这两个周期我们都称之为扫视/漂移周期（SDC），被认为是收集信息的关键 关于视觉场景和其中的物体。我们将记录单个单位和局部场电位（LFP）活动 同时从V1 - 2 - 4区域的中央凹投影中提取，同时猴子执行匹配样本任务， 形状或纹理，形状充满纹理，以了解SDC在皮层处理过程中的作用。 主动视觉通过使用凝视稳定，我们将能够在大脑皮层中破坏视网膜输入。 任务选择性地探索SDC如何控制早期视觉区域的形状和纹理加工。 在主动视觉中，SDC将空间信息映射到神经活动的时间模式。近期造型 和心理物理学的工作已经确定周期的初始瞬时阶段编码粗糙的特征， 而后期更长的漂移周期对于提取精细细节是关键的。我们感兴趣的是 在SDC中从粗处理到细处理的过渡在局部和区域间皮层网络中实现。 我们假设在SDC期间这些网络中的处理开始于瞬时前馈阶段 活动，随后是一个较长的阶段的经常性和再入活动，与伽马振荡相一致， 网络。我们假设形状是在SDC的初始阶段捕获的，并且 形状的边界和形状内的区域由漂移期间的处理的循环阶段确定。我们也建议 大脑皮层使用SDC序列来积累信息， 皮层，就其层次结构和用于通信的时间频带而言，取决于 序列由扫视和漂移（看）或由微扫视和漂移（注视）主导。 在AIM 1中，我们专注于相位锁定到SDC的活动，其中形状或纹理与样本的匹配是 固定一整天该任务中的SDC将由微扫视/漂移主导。我们会要求 如果形状的精确匹配与网络中较强的SDC瞬变一致，并且如果形状的精确匹配 纹理产生鲁棒的伽马相干性，其稍后在SDC中出现。在AIM 2中，我们研究了 SDC处于寻找和注视样本匹配任务的时期，在这个任务中，猴子在每一个 尝试正确匹配的形状或纹理。通过扫视和FxEM在整个试验中获得信息， 我们要问的是，V1 - 2 - 4网络活动的动态，与这些序列相锁定，如何反映性能 和任务。在两种AIM中都将使用凝视稳定，以影响样本刺激的皮层处理。如何 SDC调节皮层区域之间的因果关系，这些相互作用的动力学将 通过一种新的分析方法，频率提取层次分解。

This project focuses on cortical mechanisms in areas V1, V2, and V4 (V1-2-4) underlying the information- processing roles of saccades and fixational eye movements (FxEMs: microsaccades and drift) in vision. Both saccades and microsaccades are followed by drift. Sequences of saccade/drift and microsaccade/drift cycles, both of which we will refer to as saccade/drift cycles (SDC’s), are believed to be critical to gathering information about visual scenes and the objects within them. We will record single-unit and local field potential (LFP) activity from the foveal projection in areas V1-2-4 simultaneously while monkeys perform match-to-sample tasks for shape or texture, with shapes filled with texture, to understand the role of SDC’s in cortical processing during active vision. Through the use of gaze-stabilization, we will be able to disrupt the retinal input to the cortex during the task to probe selectively how SDC’s control processing of shape and texture in early visual areas. In active vision, the SDC maps spatial information into temporal patterns of neural activity. Recent modeling and psychophysical work have determined that the initial transient phase of the cycle encodes coarse features, while the later, more prolonged period of drift is critical for extracting fine details. We are interested in how the transition from coarse to fine processing in the SDC is implemented in local and inter-areal cortical networks. We hypothesize that processing in these networks during the SDC begins with a phase of transient feedforward activity followed by a longer phase of recurrent and re-entrant activity that coincides with gamma oscillations in the networks. We hypothesize that shape is captured in the initial phase of the SDC and finer features of the shape’s border and the region within the shape by the recurrent phase of processing during drift. We also suggest that the cortex uses sequences of SDC’s to accumulate information and that the organization of networks within the cortex, in terms of their hierarchy and temporal frequency band for communication, depends on whether the sequence is dominated by saccades and drift (looking) or by microsaccades and drift (fixating). In AIM 1, we focus on activity phase-locked to the SDC where the match to sample for shape or texture is fixed for an entire day’s session. The SDC’s in this task will be dominated by microsaccades/drifts. We will ask if accurate matches for shape coincide with stronger SDC transients in the network and if accurate matches for texture produce robust gamma coherence that emerges later in the SDC. In AIM 2, we investigate sequences of SDC’s in periods of looking and fixating for a match-to-sample task where the monkey is given a cue on every trial for the correct match of shape or texture. Information is gained across the trial through saccades and FxEMs, and we ask how the dynamics of V1-2-4 network activity, phase-locked to these sequences, reflect performance and task. Gaze-stabilization will be used in both AIMs to impact cortical processing of the sample stimulus. How the SDC’s modulate the causal relationships between cortical areas and the dynamics of those interactions will be examined through a new method of analysis, frequency-extracted hierarchical decomposition.