Statistical Analysis of Neural Data (SAND9)

第九届神经数据统计分析国际研讨会（SAND 9）将于2019年5月21日至23日在宾夕法尼亚州匹兹堡举行。其目的是定义神经数据分析中的重要挑战和攻击它们的有用策略。SAND 9将汇集对神经数据定量分析感兴趣的神经生理学家、统计学家、数学家、工程师、物理学家和计算机科学家。会议将包括8个主题演讲，5个特邀讨论者，12个讲座由初级研究人员根据摘要提交，海报会议和小组讨论脑科学多个领域的新挑战竞争性选择。SAND 9是2002年开始的系列研讨会中的第九次。这些研讨会在春季在卡内基梅隆大学和匹兹堡大学联合运营的认知神经基础中心举行。与过去的SAND会议一样，SAND 9旨在促进实验神经科学家与那些接受过统计和计算方法培训的人之间的沟通;鼓励年轻的研究人员展示他们的工作并从更资深的研究人员那里获得反馈;并将女性，代表性不足的少数民族和残疾人纳入参与者，他们可能会从小型研讨会环境中受益。用于发现行为的神经基础的实验方法一直在迅速发展，因此，大脑数据集的大小和复杂性都在增加。迫切需要更好地理解这些丰富数据集的方法，这些方法可以设计出更多信息的实验。本系列研讨会关注的是识别、讨论和传播许多最有前途的方法来分析各种神经数据，包括从显微镜、电生理学、钙成像、电路刺激和神经成像获得的数据。更多细节可在www.example.com上获得http://sand.stat.cmu.edu/This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The ninth international workshop on Statistical Analysis of Neural Data (SAND9) will take place May 21-23, 2019, in Pittsburgh, PA. Its purpose is to define important challenges in the analysis of neural data and useful strategies for attacking them. SAND9 will bring together neurophysiologists, statisticians, mathematicians, engineers, physicists, and computer scientists who are interested in quantitative analysis of neural data. The meeting will include 8 keynote lectures, with 5 invited discussants, 12 lectures by junior investigators chosen competitively based on abstract submissions, a poster session, and a panel discussion of emerging challenges across several areas of brain science. SAND9 is the ninth workshop in a series that began in 2002. The workshops are held during the spring at the Center for the Neural Basis of Cognition, run jointly by Carnegie Mellon University and the University of Pittsburgh. Like past SAND meetings, SAND9 aims to foster communication between experimental neuroscientists and those trained in statistical and computational methods; to encourage young researchers to present their work and get feedback from more senior investigators; and to include as participants women, under-represented minorities, and persons with disabilities, who might benefit from the small workshop environment. Experimental methods for discovering the neural basis of behavior have been advancing rapidly and, as a result, brain data sets are increasing in size and complexity. Methods for better understanding such rich data sets, which can enable design of ever-more informative experiments, are desperately needed. This workshop series is concerned with identifying, discussing, and disseminating many of the most promising approaches to analysis of neural data of all kinds, including data obtained from microscopy, electrophysiology, calcium imaging, circuit stimulation, and neuroimaging. More details are available at http://sand.stat.cmu.edu/This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.