Dynamic Signal Processing Analyses of Neural Plasticity

描述（由申请人提供）：为了响应PAR-02-010，我们提议在统计学家（Emery N.布朗的马萨诸塞州总医院，合作伙伴主任），神经科学实验学家（博士马修A。Wilson和纽约大学的Wendy Suzuki博士）和控制工程师（新南威尔士大学的维克托Solo博士）开发了一种系统工程方法来理解神经可塑性。生物工程的研究领域将是通过结合点过程理论和自适应估计来研究学习和记忆形成过程中的神经可塑性，从而开发神经信号处理算法。实验研究将研究大鼠、基因改变小鼠和灵长类动物海马和邻近内侧颞叶结构（内嗅、嗅周和海马旁皮质）内神经活动的动力学。这些实验研究将提供一个集中的调查，设计新的神经信号处理方法，适用于多个同时记录的神经尖峰列车的动态分析的基础。我们设计的算法将用于分析在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所设计的方法适用于所收集的数据。该合作伙伴关系的长期目标是：建立一种综合实验信号处理方法，以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，用于分析神经系综的动态行为;并建立跨学科的研究环境，研究生和博士后研究员可以在尖端实验方法和信号处理技术方面得到很好的培训，这些方法和技术是研究诸如大脑中神经信息编码的动力学等复杂问题所共同需要的。这项工作对健康的影响是对神经可塑性的更基本的理解，因此，它如何影响正常的生理过程，如生长，发育和学习以及病理条件，如药物成瘾，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导致创造机器-大脑接口和设计神经假体设备的创新方法。

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.