Algorithms and Informatics for Analysis of Neural Coding

神经编码分析的算法和信息学

• 批准号: 6863633
• 负责人: DANIEL GARDNER
• 金额: $ 57.14万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6863633
• 关键词: Invertebrata; bioengineering /biomedical engineering; bioinformatics; computational biology; computational neuroscience; computer system design /evaluation; information systems; neural information processing; neural transmission; neurobiology

DESCRIPTION (provided by applicant): The proposed project will establish a multifaceted resource and collaboration to support a broad range of users, including experimental and computational neuroscientists, neuroinformatics developers, and others with an interest in the central problem of neural coding. The project supports as well the Human Brain Project initiative by incorporating linked neuroscience, informatics, and merged neuroinformatics goals. The neuroscience goal is an understanding of neural coding, synthesized via application of a wide range of algorithms to a database of neural data from multiple cortical areas, protocols, and preparations. Subgoals quantify the information present in patterns of visual neural activity, determine which features of activity carry information and transmit it between neurons and cortical areas, test contrasting coding hypotheses, and relate information and activity to behavior. Informatics goals include implementation and application of new analytic algorithms and the development, interfacing, and availability of a parallel computational resource, enhancing the value of a linked neurodatabase by adding value to archived data and encouraging submission and data sharing. The neuroinformatics goal tests different analytic algorithms against particular neurobiological processes, sites, and paradigms, including parallel channels formed by paired neurons. Neural coding is imperfectly understood. A main limitation is that at present, analyses and models are often derived from or tested on restricted numbers of datasets. To remove these limits, the project will link four disciplines: experimental neuroscience, analytical computational neuroscience, computer science, and informatics, via three aims. The first aim will develop, implement, and refine an array of algorithms for neural data analysis that probe how information is represented and processed. By applying several approaches to a given dataset, or one approach to many datasets, investigators can reach conclusions that are robust and not method-dependent. Lack of available computing power often limits the use of such algorithms. The second aim applies an existing 26-processor parallel computational array and designs neuroinformatics tools to ease data exchange with neurodatabases. Parallelized algorithms will thus be applied to a broad range of neural data. Utilizing insights into neural coding and parallel processing, the third aim will refine and advance algorithmic development, parallelization schemes, and neuroinformatic classification and exchange. New experimental/analytic collaborations will advance the design of protocols for the study of visual and somatic sensation that produces neural data--especially multiunit data--readily analyzed using evolving algorithms. Generated as well will be new ideas for hypothesis-testing analytics, all advancing collaborative computational neuroinformatics.

该项目将建立一个多方面的资源和合作，以支持广泛的用户，包括实验和计算神经科学家，神经信息学开发人员以及其他对神经编码中心问题感兴趣的人。该项目还支持人脑项目倡议，将神经科学，信息学和合并的神经信息学目标相结合。神经科学的目标是理解神经编码，通过将广泛的算法应用于来自多个皮层区域，协议和准备的神经数据数据库来合成。子目标量化视觉神经活动模式中存在的信息，确定哪些活动特征携带信息并在神经元和皮层区域之间传递信息，测试对比编码假设，并将信息和活动与行为联系起来。信息学的目标包括新的分析算法的实施和应用，以及并行计算资源的开发，接口和可用性，通过增加存档数据的价值来提高链接神经数据库的价值，并鼓励提交和数据共享。神经信息学的目标是针对特定的神经生物学过程、位点和范式测试不同的分析算法，包括由成对神经元形成的平行通道。神经编码尚未完全理解。一个主要的局限性是，目前，分析和模型往往来自或测试的数据集数量有限。为了消除这些限制，该项目将通过三个目标将四个学科联系起来：实验神经科学，分析计算神经科学，计算机科学和信息学。第一个目标是开发、实现和完善一系列用于神经数据分析的算法，以探索信息是如何被表示和处理的。通过对给定数据集应用多种方法，或对多个数据集应用一种方法，研究人员可以得出稳健且不依赖于方法的结论。缺乏可用的计算能力往往限制了这种算法的使用。第二个目标是应用现有的26处理器并行计算阵列，并设计神经信息学工具，以简化与神经数据库的数据交换。因此，可编程算法将被应用于广泛的神经数据。利用对神经编码和并行处理的见解，第三个目标将改进和推进算法开发、并行化方案以及神经信息学分类和交换。新的实验/分析合作将推进视觉和躯体感觉研究协议的设计，这些协议产生神经数据-特别是多单元数据-易于使用不断发展的算法进行分析。假设检验分析的新想法也将产生，所有这些都将推进协同计算神经信息学。