Neural Computation in the Retina

描述（由申请人提供）：这个项目的主要目标是扩展和加深我们对神经回路特性的理解。选择脊椎动物视网膜作为模型系统，因为它易于实验获取和其复杂的解剖结构。当神经元连接在一起形成一个回路时，两个特性变得重要：首先，神经元的活动在动物的环境中有一定的意义；其次，神经元对它们的输入进行计算。因此，我们将研究视网膜如何编码和处理视觉信息。具体目标是：1)利用信息理论技术对视网膜神经节细胞进行功能分类；2)获取自然电影片段数据库并对其统计数据进行分类；3)构建脉冲词字典以有效捕获视网膜中的非线性处理；4)研究视网膜适应的普遍性并表征其对神经编码的影响。对视网膜图像处理过程的详细了解是神经科学的基本兴趣所在，对视网膜假体成功恢复视力也很重要。

DESCRIPTION (provided by applicant): The broad goals of this project are to extend and deepen our understanding of the properties of neural circuits. The vertebrate retina is chosen as a model system, because of its ease of experimental access and its complex anatomy. When neurons are hooked together into a circuit, two properties become important: first, the activity of the neurons means something in the context of the animal; second, neurons perform a computation on their inputs. We thus will study how the retina encodes and processes visual information. The specific aims are: 1) perform a functional classification of retinal ganglion cells using information theoretic techniques, 2) acquire a database of natural movie clips and categorize their statistics, 3) build a spike word dictionary to efficiently capture nonlinear processing in the retina, and 4) investigate the generality of retinal adaptation and characterize its effects on the neural code. A detailed knowledge of what image processing occurs in the retina is of fundamental interest to neuroscience and is also important for a retinal prosthesis to be able to restore vision successfully.