Algorithms and Informatics for Analysis of Neural Coding

描述(由申请人提供)：拟议的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的中心问题感兴趣的人。该项目还通过结合相互关联的神经科学、信息学和合并的神经信息学目标来支持人脑项目倡议。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质区域、方案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，确定活动的哪些特征携带信息并在神经元和皮质区域之间传递信息，测试对比编码假设，并将信息和活动与行为联系起来。信息学的目标包括实施和应用新的分析算法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连接的神经数据库的价值，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、位置和范例测试不同的分析算法，包括由成对神经元形成的平行通道。人们对神经编码的理解还不够完善。一个主要的限制是，目前的分析和模型往往来自有限数量的数据集或在其上进行测试。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。第一个目标将开发、实施和改进一系列用于神经数据分析的算法，这些算法探索信息是如何表示和处理的。通过将几种方法应用于给定的数据集，或将一种方法应用于多个数据集，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会限制此类算法的使用。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，并行化算法将应用于更广泛的神经数据。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分类和交换。新的实验/分析合作将推动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--易于使用进化算法进行分析。还将产生假设检验分析的新想法，所有这些都促进了协作计算神经信息学的发展。

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.