Circuit mechanisms for encoding naturalistic motion in the mammalian retina

哺乳动物视网膜中编码自然运动的电路机制

• 批准号: 10456779
• 负责人: Wei Wei
• 金额: $ 64.65万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456779
• 关键词: Address; Algorithms; Animals; Brain; Brain region; Calcium; Cells; Complex; Computational algorithm; Conscious; Cues; Data; Detection; Disease; Disinhibition; Electrophysiology (science); Environment; Exhibits; Eye Movements; Fire - disasters; Goals; Image; Knowledge; Laboratories; Lateral; Link; Measures; Modeling; Motion; Movement; Mus; Mutant Strains Mice; Neurons; Neurosciences; Noise; Output; Pattern; Performance; Property; Reporting; Research; Retina; Sensory; Signal Transduction; Stimulus; Synapses; Testing; Trees; Vision; Visual; Visual system structure; Wild Type Mouse; Work; base; feature detection; ganglion cell; genetic manipulation; insight; neuromechanism; patch clamp; preservation; prevent; relating to nervous system; resilience; response; sensory input; visual dysfunction; visual processing; visual stimulus

Abstract Motion detection, a fundamental computation of the visual system, begins in the retina. In the mammalian retina, the direction of moving objects is computed by the direction-selective circuit. The retinal output of this circuit is provided by direction-selective ganglion cells (DSGCs). These cells are strongly activated by motion in their preferred direction, but are suppressed by motion in the opposite, or “null”, direction. They report the direction of motion to higher brain centers for further visual processing, and they contribute to the control of eye movements and conscious vision. Besides their direction selectivity, DSGC responses are prominently influenced by the context of visual environments. These context-dependent properties are central to the motion encoding by DSGCs in the natural environment. This proposal aims to address two important context-dependent circuit functions pertinent to naturalistic stimuli. The first one is noise resilience. Motion in natural scenes is often accompanied by the presence of other visual features or “noise”. Aim 1 will determine circuit mechanisms that preserve direction selectivity in the presence of background noise. The second function is the encoding of motion contrast. Due to constant body and eye movements, visual inputs on the retina are composites of global image shifts and relative motion between moving objects and their backgrounds. DSGC responses are not only direction-selective, but also sensitive to relative motion compared to global motion. Aim 2 will determine the circuit motifs that confer DSGCs sensitivity to motion contrast. In Aim 3, we will link the algorithmic functions of experimentally defined circuit motifs to the encoding performance of DSGCs to naturalistic motion stimuli. Our proposed work combining functional, connectomic, computational and theoretical approaches is expected to produce a multi-layered circuit model that dynamically engages distinct circuit components for context-dependent processing of naturalistic motion, a dramatic departure from the current static circuit model of retinal feature selectivity. Since the connectivity patterns in the retina consist of canonical circuit motifs that recur across brain regions and animal species, our study will provide insights into the general principles of neural computation by the algorithmic functions of elementary circuit motifs.

抽象的 运动检测是视觉系统的基本计算，从视网膜开始。在 在哺乳动物视网膜中，移动物体的方向是由方向选择电路计算的。 该电路的视网膜输出由方向选择性神经节细胞（DSGC）提供。这些 细胞会因沿其偏好方向的运动而被强烈激活，但会被沿其偏好方向的运动所抑制。 相反或“零”方向。他们向高级大脑中心报告运动方向 进一步的视觉处理，它们有助于控制眼球运动和意识 想象。除了方向选择性之外，DSGC 响应还受到以下因素的显着影响： 视觉环境的背景。这些与上下文相关的属性是运动的核心 DSGC 在自然环境中进行编码。该提案旨在解决两个重要问题 与自然刺激相关的上下文相关电路功能。第一个是噪音 弹力。自然场景中的运动通常伴随着其他视觉特征的存在 或“噪音”。目标 1 将确定保持方向选择性的电路机制 存在背景噪音。第二个功能是运动对比度的编码。由于 身体和眼睛不断运动，视网膜上的视觉输入是全局图像的合成 移动物体与其背景之间的位移和相对运动。 DSGC 响应为 不仅具有方向选择性，而且与全局运动相比，对相对运动也很敏感。目的 图 2 将确定赋予 DSGC 对运动对比度敏感度的电路基序。在目标 3 中，我们 将实验定义的电路图案的算法功能与编码联系起来 DSGC 对自然运动刺激的性能。我们提出的工作结合了功能性、 连接组学、计算和理论方法预计将产生多层 动态接合不同电路组件以实现上下文相关的电路模型 自然运动的处理，与当前的静态电路模型截然不同 视网膜特征选择性。由于视网膜中的连接模式由规范电路组成 跨大脑区域和动物物种重复出现的主题，我们的研究将提供对 通过基本电路图案的算法函数进行神经计算的一般原理。