Neural Computation for Innate Behaviors in the Superior Colliculus

上丘先天行为的神经计算

• 批准号: 10438649
• 负责人: MARKUS MEISTER
• 金额: $ 37.84万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10438649
• 关键词: Amphibia; Anatomy; Animals; Area; Attention; Behavior; Behavioral; Birds; Brain; Brain Diseases; Cells; Computer Models; Data; Detection; Environment; Eye; Failure; Familiarity; Fishes; Goals; Hippocampus (Brain); Human; Image; Instinct; Interdisciplinary Study; Knowledge; Label; Laboratory mice; Link; Literature; Location; Mammals; Memory; Mental disorders; Methods; Modeling; Molecular Genetics; Motor; Movement; Mus; Neurons; Neurosciences; Optics; Output; Pathway interactions; Process; Research; Research Project Grants; Resources; Retina; Retinal Ganglion Cells; Role; Sensory; Signal Transduction; Specificity; Stimulus; Testing; Time; Vertebrates; Vision; Visual; Visual Cortex; Visual Fields; Work; awake; base; cell type; comparative; directed attention; image processing; multisensory; neural circuit; neural model; neurotransmission; novel; programs; relating to nervous system; response; sensory input; superior colliculus Corpora quadrigemina; tool; visual information; visual memory; visual processing; visual stimulus

Neural computation for innate behaviors in the superior colliculus The long-term goal of the proposed research is to understand how the brain makes sense of the onslaught of sensory data to extract just the few bits of relevant knowledge needed to make a decision. Specifically we will focus on innate behaviors of the laboratory mouse, such as the escape from a threat, the pursuit of small prey, and visual navigation. These are robust and reliable behaviors that require rapid and sensitive detection and localization of specific features in the visual scene. Mammals have two brain areas dedicated to visual processing: the thalamo-cortical pathway and the superior colliculus. The superior colliculus (SC) is the evolutionarily more ancient pathway, shared with non-mammalian vertebrates. It receives direct sensory input from the retina, and its output neurons produce motor signals that can steer the animal's movements. In general terms, the superior colliculus is thought to identify the most salient points in the scene for the purpose of overt orienting actions. It may also direct the orienting of covert neural resources, like when we “pay attention” to a location in the scene. In recent research we identified three aspects of neural computation in the SC that extend much beyond the processing performed in the retina. On the background of this work, combined with earlier analysis, we developed a hypothetical model of neural processing within the SC. Here we propose to test key aspects of this model, with the goal of developing its quantitative details such that it correctly accounts for the extraction of salient image features from the visual scene. The research combines behavioral methods, large-scale electrical and optical recordings of neural activity, manipulations of neural circuitry, circuit tracing tools, and circuit modeling. We will pursue the following aims: (1) Understand the unusual functional anatomy of the SC whereby different parts of the visual field emphasize different visual features. (2) Determine how the SC achieves the extraction of select visual features with both specificity and invariance. (3) Understand an intriguing form of image memory in the SC that suppresses the response to familiar stimuli. Two common themes connect these aims: Do these neural computations require a contribution from the visual cortex? And what is the role of different cell types in the underlying neural circuits? If these aims are achieved, they will contribute to an integrated understanding of how the brain reliably and automatically identifies the most salient features of the environment, an essential function for our interactions with the world. In turn that will help illuminate failure of that process, as occurs in various psychiatric disorders of attention.

上丘的天生行为的神经计算 拟议研究的长期目标是了解大脑如何了解 感觉数据仅提取做出决定所需的相关知识的几位。特别是我们会的 专注于实验室老鼠的先天行为，例如逃脱威胁，追求小猎物， 和视觉导航。这些是需要快速和敏感检测的坚固且可靠的行为 视觉场景中特定功能的定位。 哺乳动物有两个用于视觉处理的大脑区域：丘脑 - 皮层途径和上级 丘。上丘（SC）是进化上更古老的途径，与非哺乳动物共享 脊椎动物。它从视网膜接收直接感官输入，其输出神经元产生电动机信号 可以引导动物的运动。一般而言，超级胶囊被认为可以识别最多 场景中的重要点是为了明显的定向动作。它也可能指导封面的定位 神经资源，就像我们“注意”现场的位置时。 在最近的研究中，我们确定了SC中神经计算的三个方面，这些方面超出了 在视网膜中进行的处理。在这项工作的背景下，再加上早期分析，我们 在SC中开发了一种神经加工的假设模型。在这里，我们建议测试 该模型的目的是开发其定量细节，以便正确解释 视觉场景中的显着图像特征。 该研究结合了行为方法，神经活动的大规模电和光学记录， 神经电路，电路跟踪工具和电路建模的操作。我们将追求以下目标： （1）了解SC的异常功能解剖结构，该解剖学强调视野的不同部分 不同的视觉特征。 （2）确定SC如何实现所选视觉特征的方法 特异性和不变性。 （3）了解SC中图像记忆的一种有趣的形式，该形式抑制了 对熟悉的刺激的反应。两个常见的主题连接以下目的：这些神经计算需要一个 视觉皮层的贡献？不同的细胞类型在潜在的神经回路中的作用是什么？ 如果实现这些目标，它们将有助于对大脑的可靠和方式的综合理解 自动识别环境的最显着特征，这是我们互动的重要功能 与世界。反过来，这将有助于阐明该过程的失败，如各种精神疾病 注意。