Analyzing Neural Activity Using Information Theory

使用信息论分析神经活动

基本信息

批准号：
6540656
负责人：
WILLIAM B LEVY
金额：
$ 11.1万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2001
资助国家：
美国
起止时间：
2001-07-01 至 2003-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6540656
关键词：
brain disorders cognition computational neuroscience computer program /software computer simulation computer system design /evaluation information theory neural information processing neurophysiology

项目摘要

DESCRIPTION (Adapted from the applicant's abstract): If the purpose of the nervous system is information processing, then we should be able to say what it means for a biological system to process information. Unfortunately, little can be said in a quantitative way. To address this question attempts have intermittently been made over the last 50 years to understand brain function by applying information theory (IT). In recent years, such attempts have become more frequent, with occasional, interesting successes. However, from the viewpoint of the engineering discipline of IT, these attempts have hardly scratched the surface of what IT has to offer, and so far, people have only attempted to incorporate the most well-known and elementary aspects of IT. Indeed, the ideas applied with apparent success - source coding theory - are essentially as old as Shannon's original work. Since that time much has occurred in IT, including several extensions of Shannon's work. Here the investigators advocate the introduction of RD theory and its recent offspring, successive refinement theory. It is the goal of the proposed research to create a demonstration illustrating the applicability and promising superiority of these more sophisticated results of IT. The investigators will translate the ideas of rate-distortion theory and successive refinement theory from the communication literature, where they were developed, to the issues of biological computation. This translation will necessarily be abstract and mathematical. At the same time, however, the investigators will further show people how to apply these insights as well as test several conjectures. Biologically motivated computer simulations of small examples, examples well within the purview of neural network theory and the issues inherent in studying the computational basis of cognition, will be used to illustrate the theory being developed. Finally, the investigators describe how RD theory and the dynamics inherent in the translated version of successive refinement theory can be used to quantify some of the most pervasive metaphors of neural computation. Thus although there is tremendous promise in using the known results of IT, the investigators believe that neuroscientists must begin by applying some of the deeper aspects of the theory. In particular, the overly simplistic uses of IT which now exist in the neuroscientific literature must be clarified and upgraded. The proposed approach is innovative because it has not been done before in the way proposed and it is important to the mission of NIH because understanding the brain is important to the mission of NIH. Many diseases and disabilities result from impaired or damaged brain function. The list is long: drug abuse, blindness, memory and cognitive impairments due to aging, deafness, learning disabilities in children, all types mental illness, post-traumatic stress disorder, epilepsy, traumatic brain injury, stroke, etc. If the goal is to repair, prevent, and treat such maladies, a fundamental, deep understanding of what neuronal activity signifies and what this activity means in terms of thought processes, sensations, motivations, and our ability to affect the world will benefit from an improved theory neural information processing.

描述（改编自申请人的摘要）：如果目的神经系统是信息处理的，那么我们应该可以说什么呢它意味着生物系统处理信息。很遗憾，几乎无法定量地描述。为了解决这个问题尝试在过去 50 年中断断续续地进行了研究以了解大脑通过应用信息论（IT）发挥作用。近年来，此类尝试已经变得越来越频繁，偶尔也会取得有趣的成功。然而，从IT工程学科的角度来看，这些尝试几乎没有触及 IT 所提供的内容的表面，到目前为止，人们已经了解了只是试图将最广为人知和最基本的方面纳入其中它。事实上，应用得明显成功的思想——源编码理论——是基本上与香农的原作一样古老。从那时起，很多发生在 IT 领域，包括香农工作的几个扩展。这里的研究人员主张引入 RD 理论及其最新的产物，逐次细化理论。拟议研究的目标是创建一个演示，说明其适用性和前景这些更复杂的 IT 成果的优越性。调查人员将转化率失真理论和逐次求精理论的思想从传播文献（它们的发展地）到问题生物计算。这个翻译必然是抽象的并且数学的。但与此同时，调查人员将进一步表明人们如何应用这些见解并测试一些猜想。生物驱动的计算机模拟小例子，例子很好在神经网络理论的范围内和固有的问题研究认知的计算基础，将用于说明理论正在发展。最后，研究人员描述了 RD 理论和连续细化翻译版本中固有的动态理论可以用来量化一些最普遍的神经隐喻计算。因此，尽管使用已知的技术有巨大的希望根据 IT 的结果，研究人员认为神经科学家必须首先应用该理论的一些更深层次的方面。特别是，过度神经科学文献中现有的信息技术的简单化使用必须予以明确和升级。所提出的方法是创新的，因为它在以前从未被采用过提出的方式，这对 NIH 的使命很重要，因为理解大脑对于 NIH 的使命非常重要。许多疾病和残疾大脑功能受损或受损的结果。名单很长：滥用药物、由于衰老、耳聋、学习而导致失明、记忆和认知障碍儿童残疾、所有类型的精神疾病、创伤后应激障碍疾病、癫痫、脑外伤、中风等。如果目标是修复、预防和治疗此类疾病，需要对这些疾病有基本的、深入的了解神经元活动意味着什么以及该活动意味着什么思维过程、感觉、动机以及我们影响事物的能力世界将受益于改进的神经信息处理理论。