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: 9788123
• 负责人: Wei Wei
• 金额: $ 65.13万
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788123
• 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; Functional disorder; Goals; Image; Knowledge; Laboratories; Lateral; Link; Measures; Modeling; Motion; Movement; Mus; Mutant Strains Mice; Neurons; Neurosciences; Noise; Output; Pattern; Performance; Property; Reporting; Research; Retina; Retinal; 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 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.

摘要 运动检测是视觉系统的基本计算，它始于视网膜。在 在哺乳动物视网膜中，移动物体的方向由方向选择电路计算。 该回路的视网膜输出由方向选择性神经节细胞（DSGCs）提供。这些 细胞在其优选方向上的运动强烈激活，但在其优选方向上的运动被抑制。 相反或“零”方向。它们将运动的方向报告给更高级的大脑中心， 进一步的视觉处理，它们有助于控制眼球运动和意识 视野除了它们的方向选择性之外，DSGC响应显著地受 视觉环境的背景。这些依赖于上下文的属性是运动的核心 在自然环境中由DSGCs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 resilience.自然场景中的运动往往伴随着其他视觉特征的存在 或“噪音”。目标1将确定电路机制，保持方向选择性， 存在背景噪音。第二个功能是运动对比度的编码。由于 持续的身体和眼睛运动，视网膜上的视觉输入是全局图像的合成 移动物体和它们的背景之间的相对运动。DSGC的答复是 不仅是方向选择性的，而且与全局运动相比对相对运动敏感。目的 2将确定赋予DSGCs对运动对比敏感性的电路图案。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 DSGCs对自然运动刺激的表现。我们提出的工作结合功能， 连接，计算和理论方法有望产生一个多层次的 一种电路模型，它动态地使用不同的电路组件， 自然主义运动的处理，一个戏剧性的偏离目前的静态电路模型， 视网膜特征选择性由于视网膜中的连接模式由典型回路组成， 在大脑区域和动物物种中反复出现的图案，我们的研究将为我们提供深入的了解。 神经计算的一般原理，通过基本电路图案的算法函数。