DEVELOPMENT OF WIENER THEORY FOR NETWORK ANALYSIS

网络分析维纳理论的发展

• 批准号: 3417679
• 负责人: KEN-ICHI NAKA
• 金额: $ 7.36万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-01-01 至 1995-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3417679
• 关键词: alternatives to animals in research; amacrine cells; catfish; computational neuroscience; computer program /software; electrophysiology; horizontal cell; light adaptations; neural information processing; neural transmission; photostimulus; retina; retinal bipolar neuron; retinal ganglion; visual perception

In 1827, plant biologist Robert Brown discovered what is known as Brownian motion, a class of (mathematically defined) chaos. The formal derivative of Brownian motion is Gaussian white-noise. In 1958, Nobert Wiener proposed to use the Gaussian white-noise as an input probe to identify a system by a series of orthogonal functionals, the Wiener G-functionals. Our past studies have shown that white-noise analysis is uniquely suited for studying the response dynamics of the retinal neuron network. Here we propose to refine further this analytical tool by taking advantage of recent theoretical developments. Newer methodology will be applied to study signal transformation in the vertebrate inner retinal network as well as information compression taking place in the ganglion cells where the analog signals are translated into a point process, spike discharges.

1827 年，植物生物学家罗伯特·布朗发现了所谓的布朗理论 运动，一类（数学定义的）混沌。 形式导数 布朗运动是高斯白噪声。 1958 年，诺伯特·维纳 建议使用高斯白噪声作为输入探针来识别 系统由一系列正交泛函（维纳 G 泛函）组成。 我们过去的研究表明，白噪声分析非常适合 用于研究视网膜神经元网络的响应动力学。 在这里我们 建议利用以下优势进一步完善该分析工具 最近的理论发展。 较新的方法将应用于 还研究脊椎动物视网膜内部网络中的信号转换 当信息压缩发生在神经节细胞中时 模拟信号被转换为点过程，即尖峰放电。