Neural Computation in the Retina

描述（由申请人提供）：该项目的主要目标是了解可以由神经电路执行的计算。选择脊椎动物视网膜作为模型系统，因为我们能够在用逼真的光模式刺激的同时实现对其输出的完整记录，以及其复杂且特征良好的解剖结构。视网膜对视觉刺激的表示在光感受器和神经节细胞之间发生显著变化，光感受器使用类似照相机的表示，其中每个细胞都是一个像素，神经节细胞用复杂且高度重叠的视觉特征基组表示图像。我们试图表征的方式，其中视觉场景所代表的视网膜神经节细胞的完整人口。 具体目标是：1）通过执行功能分类来表征神经节细胞群体内所表示的视觉特征的多样性和刻板性; 2）测量平行视觉通道内和平行视觉通道之间的相关性; 3）制定解码策略以使用大量神经节细胞来执行视觉辨别。我们的功能分类将通过同时记录来自单个视网膜的200多个神经节细胞来最大限度地减少从一个视网膜准备到下一个视网膜准备的细微变化的影响，并将使用信息理论来评估功能差异，使用一组视觉刺激，包括自然电影。我们的特征的模式之间的相关性神经节细胞组将使用新的信息理论的技术，包括最大熵的方法。最后，我们的解码器将寻求利用我们的相关模型在大人口，以提高视觉辨别的准确性。

DESCRIPTION (provided by applicant): The broad goals of this project are to understand the computations that can be performed by a neural circuit. The vertebrate retina is chosen as a model system, because of our ability to achieve complete 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, and the ganglion cells, which represent the image with a complex and highly overlapping basis set of visual features. We seek to characterize the manner in which visual scenes are represented by the complete population of retinal ganglion cells. 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) measure the correlation both within and among the parallel visual channels; 3) formulate decoding strategies to perform visual discrimination using large populations of ganglion cells. Our functional classification will minimize the effects of subtle variations from one retinal preparation to the next by recording simultaneously from 200+ ganglion cells from a single retina and will use information theory to assess functional differences using a battery of visual stimuli including natural movies. Our characterization of the patterns of correlation among groups of ganglion cells will use new information-theoretic techniques, including the maximum entropy approach. Finally, our decoders will seek to make use of our models of correlation in large populations to improve the accuracy of visual discriminations.