COMPUTATIONAL MODELS OF RETINAL FUNCTION

视网膜功能的计算模型

• 批准号: 3389042
• 负责人: ROBERT B BARLOW
• 金额: $ 10.73万
• 依托单位: SYRACUSE UNIVERSITY AT SYRACUSE
• 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/3389042
• 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.

我们研究计划的长期目标是了解神经系统 视觉行为的基础。 我们建议为实现这一目标而努力， 研究鲎的视觉系统。 这种动物的视网膜包含最大的神经网络， 存在定量模型。 此外，我们还了解到， 行为足够好，以精确地确定它能看到什么。 我们 计划将联合收割机理论与行为学相结合来研究神经机制 控制视觉行为的过程。 我们希望了解到 眼睛必须发送到大脑的信息才能让动物看到。 具体目标是调查： .视网膜功能的大规模并行计算模型 计算机 .行为背后视觉信息的神经编码 我们计划进行一项系统的研究，涉及计算、行为和 physiology. 到目前为止，我们已经编制了一个实际的初步模型， 1000-大规模并行计算机上的神经元视网膜，连接 机器（型号CM5）。 为了验证模型，我们开发了一种技术， 用于记录行为动物的单个视网膜神经元。 这些 技术非常适合研究信息处理 并将神经编码与视觉表现联系起来。 他们 建立鲎作为本研究合适动物模型。 过去对鲎的研究为视觉研究提供了里程碑。 拟议的研究应证明在以下领域同样富有成效： 神经编码和计算神经科学。 他们承诺会揭露 大型神经网络信息处理的基本机制 从而有助于我们了解神经元如何控制行为。