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（Robert Brown）发现了所谓的布朗尼人 运动，一类（数学定义的）混乱。 正式衍生物 布朗运动是高斯白色噪声。 1958年，诺伯特·维纳（Nobert Wiener） 建议将高斯白噪声用作输入探针来识别 系统通过一系列正交功能，即Wiener G功能。 我们过去的研究表明，白噪声分析是独特的 用于研究视网膜神经元网络的反应动力学。 我们在这里 建议通过利用 最近的理论发展。 较新的方法将应用于 脊椎动物内部视网膜网络中的研究信号转换 随着信息压缩发生在神经节细胞中 模拟信号被翻译成一个点过程，尖峰放电。