Neural Computation in the Retina

这个项目的主要目标是理解神经回路执行的一组计算。的 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，以及其复杂和良好的特点， 解剖学视网膜对视觉刺激的表征在光感受器之间发生了巨大的变化， 它使用类似照相机的表示法，其中每个细胞是一个像素，到神经节细胞，它代表 具有复杂且高度重叠的视觉特征基集的图像。我们试图了解视觉 信息是在视网膜的平行视觉通道内和之间组织的。 具体目标是：1）表征视觉特征的多样性和刻板性， 通过进行功能分类的神经节细胞的群体; 2）使用最近开发的技术 信息理论分析来测量大量神经节中的相关性和冗余性， 细胞; 3）探索如何在平行通道内和平行通道之间形成相关的群体， 执行基本的视觉任务，如空间定位，形状辨别和时间模式 识别. 视网膜神经节细胞群如何代表视觉世界的详细知识是 神经科学的根本利益，也是重要的指导视网膜的发展， 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.