CAREER: Using Advanced Statistical Techniques to Decipher the Neural Code

系统神经科学的核心问题是在大的和小的生理尺度上理解神经密码。由于缺乏足够丰富的实验数据，缺乏表征和分析数据的定量技术，以及在系统神经生理学和先进统计方法方面熟练的跨学科研究人员数量不足，进展受到限制。最近的事态发展为合作解决这些基本问题开辟了新的可能性。首先，多电极记录技术的进步使得在各种各样的实验环境中研究大型神经元群的同时活动成为可能。同样，高分辨率电压和钙敏感成像技术的最新改进现在提供了能够限制单细胞中信息处理的高度详细的生物物理模型的数据。目前的一个主要瓶颈是对这些数据的分析和定量理解。在四个方面提出了具体的方法进展：1)人口高峰列车的信息编码和解码；2)单峰列分析与最优刺激设计；3)高度精细的生物物理模型和树突成像数据的优化处理；4)稀疏神经数据的信息论分析。在每种情况下，研究者和他的研究小组将开发新的数学模型和工具，将这些模型直接拟合到观察到的数据。实施这些新技术的计算机代码将公开，以加强研究和教育的基础设施。这项工作将对新兴的神经义肢领域产生影响，这将需要我们在设计人造和真实神经组织之间的信号接口方面进行实质性的改进。了解神经元群体的编码和解码，并开发模型，使我们能够预测实验扰动对其行为的影响，这是这项努力的关键。对神经编码的研究也可能导致数学结果和统计技术，这些结果和统计技术具有独立的普遍兴趣和实用性，对信息论、图像处理、最佳过滤和点过程预测（反过来影响数百个其他学科）产生根本性影响。此外，研究者正在为统计神经科学的研究生和博士后开发高级培训课程（世界上第一个此类课程），以及本科入门课程。课堂讲稿将在网上公开，并将形成一本正在进行的高级神经数据分析教科书。培训机会将在哥伦比亚大学（加强与统计学系和理论神经科学中心的密切联系）以及与美国和国际合作者进行。

Project Summary The central problem in systems neuroscience is to understand the neural code, at both large and small physiological scales. Progress has been limited by a lack of sufficiently rich experimental data, a shortage of quantitative techniques to characterize and analyze the data, and an insufficient number of interdisciplinary researchers skilled in both systems neurophysiology and advanced statistical methods. Recent developments open new possibilities for collaborative efforts to tackle these basic problems. First, advances in multi-electrode recordings make it possible to study the simultaneous activity of large ensembles of neurons in a wide variety of experimental settings. Similarly, recent improvements in high-resolution voltage- and calcium-sensitive imaging technology now provide data capable of constraining highly-detailed biophysical models of information processing in single cells. A major bottleneck now is in analyzing and quantitatively understanding this data. Specific methodological advances in four fields are proposed: 1) encoding and decoding information in population spike trains; 2) single spike-train analysis and optimal stimulus design; 3) highly-detailed biophysical models and optimal processing of dendritic imaging data; and 4) information-theoretic analyses of sparse neural data. In each case, the investigator and his research group will develop novel mathematical models and tools for fitting these models directly to the observed data. Computer code implementing these novel techniques will be made publicly available to enhance the infrastructure for research and education. This work will have impact on the burgeoning field of neural prosthetics, which will require substantial improvements in our ability to design signaling interfaces between artificial and real neural tissue. Understanding encoding and decoding in populations of neurons and developing models that allow us to predict the effects of experimental perturbations to their behavior is key to this endeavor. This research on neural coding will also likely lead to mathematical results and statistical techniques which are of independent general interest and utility, with fundamental impacts on information theory, image processing, and optimal filtering and prediction of point processes (which in turn impact hundreds of other disciplines). In addition, the investigator is developing an advanced training course for graduate students and postdocs in statistical neuroscience (the first course of this kind in the world), as well as an introductory undergraduate course. Lecture notes will be made publicly available online and will shape a textbook in progress in advanced neural data analysis. Training opportunities will be pursued at Columbia University (strengthening already close ties with the Department of Statistics and Center for Theoretical Neuroscience) and with collaborators in the U.S. and internationally.