Retinal connectome: mobile game and crowdsourcing algorithms for EyeWire II

视网膜连接组：EyeWire II 的手机游戏和众包算法

• 批准号: 9330810
• 负责人: Hyunjune SEBASTIAN SEUNG
• 金额: $ 32.09万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-08 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9330810
• 关键词: Algorithms; Artificial Intelligence; Attention; BRAIN initiative; Behavior; Biomedical Research; Blindness; Books; Boston; Breathing; Bypass; Caenorhabditis elegans; Cells; Cellular Phone; Classification; Code; Collaborations; Communities; Computer software; Country; Crowding; Data; Development; Electrons; Event; Goals; Human; Image; Image Analysis; Institutes; Learning; Love; Maps; Modeling; Natural regeneration; Nature; Nervous system structure; Neurons; Neurosciences; New York; Nonprofit Organizations; Organism; Performance; Play; Production; Publishing; Retina; Retinal; Scheme; Science; Sensory; Social Interaction; Source; Specificity; Statistical Models; Students; Technology; Territoriality; Three-Dimensional Image; Time; Training; United States National Institutes of Health; Universities; Volunteer Group; Voting; Weight; Work; base; citizen science; connectome; crowdsourcing; design; fine art; ganglion cell; improved; innovation; microscopic imaging; neural circuit; online community; pleasure; programs; prototype; public health relevance; reconstruction; retinal prosthesis; simulation; starburst amacrine cell; success; university student; virtual; visual neuroscience; volunteer

 DESCRIPTION (provided by applicant): An online community called EyeWire proved that volunteers can be motivated to reconstruct neural circuits through an activity resembling a 3D coloring book. EyeWire helped discover space-time specificity of the wiring from bipolar cells to starburst amacrine cells, which suggested a surprising new model for direction selectivity in the retina. Motivated by this success, we are preparing to launch EyeWire II, which aims to map the entire retinal connectome, yielding the first complete wiring diagram for any region of the mammalian CNS. This ambitious goal will require innovative advances in virtually every component of EyeWire. The underlying electron microscopic image of the retina will be replaced by a new image with increased size and quality. A new artificial intelligence (AI) will be trained using a new software package for 3D deep learning. While the improved AI is expected to reduce the amount of human effort required to reconstruct a neuron, the number of neurons targeted for reconstruction will also increase dramatically. Overall, the absolute amount of human effort required will increase rather than decrease. Therefore it is critical to improve EyeWire's crowdsourcing to (1) mobilize more human effort and (2) to use human effort more efficiently. This project aims to radically improve both aspects, thereby making the retinal connectome achievable by EyeWire II. In Aim 1, we will create a compelling mobile game with the target of engaging 10x more people than the existing EyeWire community. In Aim 2, we will develop and deploy new crowdsourcing algorithms that extract wisdom from the crowd by weighted voting and optimally assign players to tasks. The Aims will be achieved through collaboration between three organizations. Wired Differently, Inc. (WD) is a new Boston-based nonprofit organization dedicated to "citizen neuroscience" that was recently spun out of MIT. WD currently operates EyeWire in collaboration with the Princeton Neuroscience Institute. The Entertainment Technology Center (ETC) at Carnegie Mellon University will offer a project-based class to its master's students to design and prototype new ideas for the mobile game. Both Aims will produce code and algorithms that will be made publicly available, and could have broad impact on citizen science. As the first crowdsourcing of 3D image analysis, the code produced by Aim 1 could be useful for the many other kinds of 3D images found in biomedical research. The crowdsourcing algorithms of Aim 2 are potentially useful for any citizen science project facing the challenge of obtaining accurate and reliable results from a heterogeneous group of volunteers.

 描述（由申请人提供）：一个名为EyeWire的在线社区证明，志愿者可以通过类似3D着色书的活动来重建神经回路。EyeWire帮助发现了从双极细胞到星状无长突细胞的时空特异性，这为视网膜的方向选择性提出了一个令人惊讶的新模型。在这一成功的推动下，我们正准备推出EyeWire II，其目标是绘制整个视网膜连接体，为哺乳动物CNS的任何区域绘制第一个完整的布线图。这一雄心勃勃的目标将需要EyeWire几乎所有组件的创新进步。视网膜的基本电子显微镜图像将被具有增加的尺寸和质量的新图像所取代。一个新的阿尔蒂官方智能（AI）将被训练 使用新的软件包进行3D深度学习。虽然改进的人工智能有望减少重建神经元所需的人力，但重建的神经元数量也将大幅增加。总的来说，所需人力的绝对数量将增加而不是减少。因此，改善EyeWire的众包至关重要，以（1）调动更多的人力资源，（2）更有效地利用人力资源。该项目旨在从根本上改善这两个方面，从而使EyeWire II可以实现视网膜连接体。在目标1中，我们将创建一个引人注目的移动的游戏，目标是吸引比现有EyeWire社区多10倍的人。在目标2中，我们将开发和部署新的众包算法，通过加权投票从人群中提取智慧，并将玩家最佳分配到任务中。这些目标将通过三个组织之间的合作来实现。Wired，Inc. (WD)是一个位于波士顿的非营利性组织，致力于“公民神经科学”，最近从麻省理工学院分离出来。WD目前与普林斯顿神经科学研究所合作运营EyeWire。卡内基梅隆大学的娱乐技术中心（ETC）将为硕士生提供一个基于项目的课程，为移动的游戏设计和原型化新的想法。这两个目标将产生代码和算法，将公开提供，并可能对公民科学产生广泛的影响。作为3D图像分析的第一个众包，Aim 1产生的代码可能对生物医学研究中发现的许多其他类型的3D图像有用。Aim 2的众包算法对于任何面临从异质志愿者群体获得准确和可靠结果的挑战的公民科学项目都有潜在的用处。