Neural Computation for Innate Behaviors in the Superior Colliculus

上级丘固有行为的神经计算 这项研究的长期目标是了解大脑是如何理解 感官数据来提取做出决定所需的少量相关知识。具体来说，我们将 专注于实验室小鼠的先天行为，如逃避威胁，追逐小猎物， 和视觉导航。这些是需要快速和灵敏检测的稳健和可靠的行为， 视觉场景中特定特征的定位。 哺乳动物有两个专门负责视觉处理的大脑区域：丘脑-皮层通路和上级 丘上级丘（SC）是进化上更古老的通路，与非哺乳动物共享 脊椎动物它接收来自视网膜的直接感觉输入，其输出神经元产生运动信号， 可以控制动物的行动一般来说，上级丘被认为是最能识别 场景中的突出点，以达到明显的定向动作的目的。它还可以指导隐蔽的 神经资源，比如当我们“注意”场景中的一个位置时。 在最近的研究中，我们确定了SC中神经计算的三个方面，这些方面远远超出了 在视网膜中进行的处理。在此背景下，结合前面的分析， 开发了一个假设模型的神经处理在SC。在这里，我们建议测试的关键方面， 该模型的目标是开发其定量细节，以便正确地解释提取 视觉场景中的显著图像特征。 这项研究结合了行为方法，大规模的神经活动的电子和光学记录， 神经电路的操作，电路跟踪工具和电路建模。我们将努力实现以下目标： (1)了解SC的不寻常的功能解剖学，即视野的不同部分强调 不同的视觉特征。(2)确定SC如何实现选择视觉特征的提取， 特异性和不变性。(3)理解SC中一种有趣的图像记忆形式，它抑制了 对熟悉刺激的反应。两个共同的主题将这些目标联系起来：这些神经计算是否需要一个 视觉皮层的作用不同类型的细胞在潜在的神经回路中起着什么样的作用？ 如果这些目标得以实现，它们将有助于全面了解大脑如何可靠地 自动识别环境中最显著的特征，这是我们互动的基本功能 与世界反过来，这将有助于阐明该过程的失败，就像在各种精神疾病中发生的那样 注意力。

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.