COMPUTATIONAL MODELS OF RETINAL FUNCTION

视网膜功能的计算模型

• 批准号: 2249120
• 负责人: ROBERT B BARLOW
• 金额: $ 14.49万
• 依托单位: SYRACUSE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-04-01 至 1996-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2249120
• 关键词: alternatives to animals in research; biological models; computational neuroscience; computer data analysis; computer program /software; computers; horseshoe crabs; neural information processing; neurons; retina; video recording system; vision; visual feedback; visual stimulus

The long-range goal of our research program is to understand the neural basis of visual behavior. We propose to work toward this goal by studying the visual system of the horseshoe crab, Limulus Polyphemus. The retina of this animal contains the largest neural network for which a quantitative model exists. In addition, we understand its visual behavior well enough to determine with precision what it can see. We plan to combine theory and behavior to study the mechanisms of neural processing that control visual behavior. We hope to learn what information the eye must send to the brain for the animal to see. Specific aims are to investigate: . Computational models of retinal function on a massively parallel computer . Neural coding of visual information underlying behavior We plan a systematic study involving computation, behavior, and physiology. To date we have programmed a preliminary model of the actual 1000-neuron retina on a massively parallel computer, the Connection Machine (Model CM5). To verify the model we have developed a technique for recording from single retinal neurons of a behaving animal. These techniques are ideally suited for investigating information processing in the retina and relating neural coding to visual performance. They establish Limulus as an appropriate animal model for this research. Past studies with Limulus have provided milestones in vision research. The proposed studies should prove equally productive in the areas of neural coding and computational neuroscience. they promise to reveal basic mechanisms of information processing in a sizable neural network and thus contribute to our understanding of how neurons control behavior.

我们研究项目的长期目标是了解神经系统