Analyzing Neural Activity Using Information Theory

描述（改编自申请人的摘要）：如果目的是 神经系统是处理信息的，那么我们应该能够说 这意味着生物系统处理信息。 不幸的是， 在数量上几乎没有什么可说的。 为了解决这个问题， 在过去的50年里， 运用信息理论（IT）。 近年来，这种尝试 变得越来越频繁，偶尔会有有趣的成功。 然而，在这方面， 从IT工程学科的角度来看，这些尝试 几乎没有触及IT所能提供的表面，到目前为止，人们已经 只是试图将最知名和最基本的方面， 了 事实上，取得明显成功的思想--源代码理论-- 基本上和香农的原创作品一样古老 从那时起， 发生在IT领域，包括香农工作的几个扩展。 这里 研究人员提倡引入RD理论及其最近的后代， 逐次精化理论 拟议研究的目标是 创建一个示范，说明适用性和有前途的 这些更复杂的IT结果的优越性。调查人员将 将率失真理论和逐次求精理论的思想 从传播文学，在那里他们被开发，到问题， 生物计算 这种翻译必然是抽象的， 数学的 与此同时，调查人员将进一步显示， 人们如何应用这些见解以及测试几种方法。 生物动机的计算机模拟的小例子，例子很好 在神经网络理论的范围内， 研究认知的计算基础，将被用来说明 理论正在发展。 最后，研究人员描述了RD理论和 在连续改进的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 虽然有很大的希望，在使用已知的 研究人员认为，神经科学家必须开始， 应用一些更深层次的理论。 特别是，过度 目前存在于神经科学文献中的对信息技术的简单使用， 澄清和升级。 所提出的方法是创新的，因为以前没有这样做过。 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 是由受损或受损的大脑功能引起的。 名单很长：药物滥用， 失明、记忆力和认知能力因衰老、耳聋、学习 儿童残疾、各类精神疾病、创伤后压力 疾病，癫痫，创伤性脑损伤，中风等，如果目标是 修复，预防和治疗这些疾病，一个基本的，深刻的理解， 神经元活动意味着什么，以及这种活动在 思维过程，感觉，动机，以及我们影响 世界将受益于神经信息处理理论的改进。

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.