Neural Computation in the Retina

这个项目的主要目标是了解神经电路执行的一组计算。这个 哺乳动物视网膜被选为模型系统，因为我们有能力实现大规模记录 它的输出，同时刺激与现实的光模式，以及它的复杂和良好的特点 解剖学。视网膜对视觉刺激的表现在光感受器之间发生了巨大的变化， 其中每个细胞都是一个像素，而神经节细胞则代表 具有复杂且高度重叠的视觉特征基础集的图像。我们试图理解视觉效果如何 信息在视网膜的平行视觉通道内和之间组织。 具体目标是：1)描述所代表的视觉特征的多样性和刻板印象 通过执行功能分类来确定神经节细胞的数量；2)使用最近发展起来的技术 的信息论分析来衡量大群体神经节的相关性和冗余性 细胞；3)探索如何利用在平行通道内和之间形成的相关种群来 执行基本的视觉任务，如空间定位、形状识别和时间模式 承认。 关于视网膜神经节细胞如何代表视觉世界的详细知识 对神经科学的基本兴趣，对指导视网膜的发展也很重要 假体成功恢复视力。

The broad goals of this project are to understand the set of computations performed by a neural circuit. The mammalian retina is chosen as a model system, because of our ability to achieve large-scale recordings of its output while stimulating with realistic patterns of light, as well as its intricate and well-characterized anatomy. The retina's representation of visual stimuli changes dramatically between the photoreceptors, which use a camera-like representation where each cell is a pixel, to the ganglion cells, which represent the image with a complex and highly overlapping basis set of visual features. We seek to understand how visual information is organized within and among the retina's parallel visual channels. The Specific Aims are: 1) characterize the diversity and stereotypy of the visual features represented within the population of ganglion cells by performing functional classification; 2) use recently developed techniques of information-theoretic analysis to measure the correlation and redundancy in large populations of ganglion cells; 3) explore how correlated populations formed both within and among parallel channels can be used to perform fundamental visual tasks, such as spatial localization, shape discrimination, and temporal pattern recognition. A detailed knowledge of how the population of retinal ganglion cells represents the visual world is of fundamental interest to neuroscience and is also important for guiding the development of a retinal prosthesis to restore vision successfully.