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，那么我们应该能够说什么这意味着生物系统处理信息。很遗憾，几乎没有用定量的方式说。解决这个问题的尝试在过去的50年中，间歇性地了解了大脑通过应用信息理论（IT）来函数。近年来，这种尝试偶尔会取得有趣的成功。然而，从工程学科的角度来看，这些尝试具有几乎没有刮擦其所提供的表面，到目前为止，仅试图纳入最知名和最基本的方面它。确实，以明显成功的思想 - 来源编码理论 - 是从本质上讲，与香农的原始作品一样古老。从那时起很多发生在其中，包括香农作品的几次扩展。在这里研究人员主张引入RD理论及其最新后代，连续的完善理论。这是拟议研究的目标创建一个示范，说明适用性和有希望的这些更复杂的结果的优越性。调查人员会翻译利率延伸理论和连续完善理论的思想从传播文献（开发的地方）到问题生物计算。该翻译必然是抽象的，并且数学。但是，与此同时，调查人员将进一步显示人们如何运用这些见解以及测试一些猜想。以生物学动机的计算机模拟小型示例，示例很好在神经网络理论的范围内以及研究认知的计算基础将用于说明理论正在发展。最后，调查人员描述了RD理论和连续完善的翻译版本中固有的动力学理论可用于量化神经最普遍的隐喻计算。因此，尽管使用已知的结果，调查人员认为神经科学家必须从应用理论的一些更深的方面。特别是它的简单用途现在存在于神经科学文献中被澄清和升级。提出的方法具有创新性，因为它以前尚未在提出的方式，这对NIH的使命很重要，因为理解大脑对NIH的使命很重要。许多疾病和残疾脑功能受损或受损的原因。清单很长：滥用药物，失明，记忆和认知障碍，由于老化，耳聋，学习儿童残疾，所有类型的精神疾病，创伤后压力疾病，癫痫，脑损伤，中风等修复，预防和治疗这种疾病，对哪些神经元活动表示什么，这项活动在思考过程，感觉，动机以及我们影响的能力世界将受益于改进的理论神经信息处理。