COMPUTATIONAL MODELS OF RETINAL AND BRAIN FUNCTION

视网膜和大脑功能的计算模型

• 批准号: 6639009
• 负责人: ROBERT B BARLOW
• 金额: $ 22.34万
• 依托单位: UPSTATE MEDICAL 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 2005-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6639009
• 关键词: action potentials; behavioral /social science research tag; binocular vision; biological models; circadian rhythms; computational neuroscience; cues; horseshoe crabs; invertebrate locomotion; neural information processing; optic nerve; parallel processing; photostimulus; retina; visual feedback; visual pathways; visual perception; visual stimulus

DESCRIPTION (Adapted from applicant's abstract): The long-range goal of the research program is to understand the neural basis of visual behavior. The principal investigator proposes to work towards this goal by studying the visual system of the horseshoe crab (Limulus polyphemus). Its retina contains the one of the largest neural networks for which a quantitative model exists. In addition, a circadian clock modulates the function of the retina, and it is relatively well known what the animal can see underwater day and night. The computational models have yielded new insights about visual coding during the day. Building on this work, the principal investigator plans to combine theoretical and experimental techniques to investigate what information the eyes send to the brain for the animal to see at night. Moreover, he plans to study how the brain decodes the information it receives from the eye. Specific aims are to investigate: 1. Retinal coding of visual information underlying behavior at night. How does the animal see so well at night? During the day, across its ensemble of optic nerve fibers, the eye functions as a global feature detector generating "neural images" of mate-like objects. Does the eye use such a distributed code at night? 2. Brain networks for target detection. How do brain networks decipher the neural codes they receive from the eye day and night? From multiple recordings of brain cells, the principal investigator will develop simplified models of brain network and then drive the models with neural images generated by his cell-based models of the retina. 3. Visual information in the animal's natural environment. The principal investigator will determine whether natural fluctuations in environmental lighting once thought to be noise increase retinal signal-to-noise performance and enhance the visibility of objects important to the animal. Preliminary experiments indicate they do. The principal investigator plans a systematic study involving computation, electrophysiology, neuroanatomy and behavior. He has developed a realistic cell-based model of the daytime state of the 1000-neuron retina. Furthermore, he verifies model predictions by recording responses from single optic nerve fibers of a behaving animal, while recording what the animals sees with a miniature underwater video camera, "CrabCam." And he has developed parallel techniques for studying brain networks. His new results and new techniques establish Limulus as a good model for analyzing the link between neural coding and visual behavior. The proposed studies promise to reveal basic information-processing mechanisms in sizable neural networks. These studies may be applicable to even more complex networks--a major goal of neuroscience.

描述（改编自申请人的摘要）： 研究计划是了解视觉行为的神经基础。 的 首席研究员建议通过研究 鲎的视觉系统。 它的视网膜含有 它是最大的神经网络之一，有一个定量模型。 此外，生物钟调节视网膜的功能，并且它是 相对来说，这种动物白天和晚上在水下都能看到什么是众所周知的。 的 计算模型已经产生了关于视觉编码的新见解， 天 在这项工作的基础上，主要研究者计划将联合收割机 理论和实验技术来研究什么信息， 眼睛发送到大脑，让动物在夜间看到。 此外，他计划 研究大脑如何解码从眼睛接收的信息。 具体 目的是调查： 1.夜间行为的视觉信息视网膜编码。 如何 动物在晚上看得很清楚？ 在白天，通过它的视觉系统 神经纤维，眼睛的功能是作为一个全球性的特征检测器，产生“神经纤维”， “mate-like对象的图像”。 眼睛使用这样的分布式代码在 晚上？ 2.目标探测的大脑网络。 大脑网络是如何破译 从眼睛接收到的神经代码 从多段录音中 主要研究人员将开发简化模型， 大脑网络，然后驱动模型与神经图像产生的他 视网膜的细胞模型。 3.动物自然环境中的视觉信息。 校长 研究人员将确定环境中的自然波动是否 曾经被认为是噪声的照明增加视网膜信噪比性能 并增强对动物重要的物体的可见性。 初步 实验表明他们做的。 首席研究员计划进行一项涉及计算的系统研究， 电生理学、神经解剖学和行为学。 他发展了一种现实的 1000个神经元视网膜白天状态的基于细胞的模型。 此外，委员会认为， 他通过记录单个视神经的反应来验证模型预测， 一个行为动物的纤维，同时记录动物所看到的， 微型水下摄像机“CrabCam。“他发展了一种平行的 研究大脑网络的技术。 他的新成果和新技术 建立Limulus作为分析神经编码之间联系的良好模型 和视觉行为。 这项研究有望揭示基本的 大规模神经网络中的信息处理机制。 这些研究 可能适用于更复杂的网络--神经科学的一个主要目标。