Dynamic Signal Processing Analyses of Neural Plasticity

神经可塑性的动态信号处理分析

基本信息

批准号：
6555670
负责人：
EMERY N BROWN
金额：
$ 109.89万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-09-30 至 2007-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): In response to PAR-02-010 we propose to form a Bioengineering Research Partnership between a statistician (Dr. Emery N. Brown of Massachusetts General Hospital, Partnership Director), neuroscience experimentalists (Dr. Matthew A. Wilson of the Massachusetts Institute of Technology and Dr. Wendy Suzuki of New York University) and a control engineer (Dr. Victor Solo of the University of New South Wales) to develop a systems engineering approach to understanding neural plasticity. The area of bioengineering research will be the development of neural signal processing algorithms by combining the theory of point processes and adaptive estimation to study neural plasticity during learning and memory formation. The experimental investigations will study the dynamics of neural activity within the hippocampus and adjacent medial temporal lobe structures (entorhinal, perirhinal and parahippocampal cortices) in rats, genetically altered mice, and primates. These experimental studies will provide the basis for a focused investigation that designs new methods for neural signal processing appropriate for dynamic analysis of multiple simultaneously recorded neural spike trains. The algorithms we design will be used to analyze the data collected in the experimental studies proposed in this investigation. The close collaboration between the experimentalists and the quantitative scientists will ensure that the methods designed are appropriate for the data collected. The long-term objectives of this partnership are: to establish a combined experimental-signal processing approach to characterizing neural plasticity and how it relates to learning, memory formation and behavior; to develop broadly applicable signal processing tools for analyzing the dynamic behavior of neural ensembles; and to establish an interdisciplinary research environment so that undergraduates, graduate students and post-doctoral fellows can be well trained in both the cutting edge experimental methods and signal processing techniques that are jointly required to study a complex question such as the dynamics of neural information encoding in the brain. The health implications of this work are a more fundamental understanding of neural plasticity and, as a consequence, how it affects normal physiological processes such as growth, development and learning as well as pathologic conditions such as drug addiction, Alzheimer's disease and chronic pain. More accurate quantitative characterizations of neural information dynamics coupled with improved signal processing algorithms may also lead to innovative approaches to creating machine-brain interfaces and designing neural prosthetic devices.

描述（由申请人提供）：为了响应 PAR-02-010，我们建议在统计学家（马萨诸塞州总医院的 Emery N. Brown 博士，合作伙伴主任）和神经科学实验学家（Matthew A.麻省理工学院的 Wilson 博士和纽约大学的 Wendy Suzuki 博士）和控制工程师（新南威尔士大学的 Victor Solo 博士）开发了一种系统工程方法来理解神经网络可塑性。生物工程研究领域将是通过结合点过程和自适应估计理论来开发神经信号处理算法，以研究学习和记忆形成过程中的神经可塑性。实验研究将研究大鼠、基因改造小鼠和灵长类动物的海马体和邻近内侧颞叶结构（内嗅、嗅周和海马旁皮质）内的神经活动动态。这些实验研究将为重点研究提供基础，设计适合对多个同时记录的神经尖峰序列进行动态分析的神经信号处理新方法。我们设计的算法将用于分析本次调查中提出的实验研究中收集的数据。实验学家和定量科学家之间的密切合作将确保设计的方法适合收集的数据。此次合作的长期目标是：建立一种组合实验信号处理方法来表征神经可塑性及其与学习、记忆形成和行为的关系；开发广泛适用的信号处理工具来分析神经网络的动态行为；建立跨学科的研究环境，使本科生、研究生和博士后能够在研究神经信息编码动力学等复杂问题所需的前沿实验方法和信号处理技术方面得到良好的培训在大脑中。这项工作对健康的影响是对神经可塑性的更根本的理解，以及它如何影响正常的生理过程，如生长、发育和学习，以及病理状况，如毒瘾、阿尔茨海默病和慢性疼痛。神经信息动力学的更准确的定量表征加上改进的信号处理算法也可能会带来创建机器大脑接口和设计神经假肢设备的创新方法。