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（由适用提供）：一个名为Eyewire的在线社区证明，可以通过重新组合3D着色书的活动来激励志愿者重建神经回路。 EyeWire帮助发现了从双极细胞到Starburst Amacrine细胞的时空特定的接线城市，这为视网膜中的方向选择性提出了惊人的新模型。在这一成功的推动下，我们准备推出EyeWire II，该II旨在绘制整个视网膜连通性，从而为哺乳动物中枢神经系统的任​​何区域提供第一个完整的接线图。这个雄心勃勃的目标将需要在眼睛的每个组成部分中进行创新的进步。视网膜的基础电子显微镜图像将被新图像所取代，并具有增加的尺寸和质量。新的艺术家智慧（AI）将接受培训 使用新的软件包进行3D深度学习。虽然改善的AI有望减少重建神经元所需的人为努力的数量，但针对重建的神经元的数量也将大大增加。总体而言，所需的人为努力的绝对数量将增加而不是减少。因此，至关重要的是，提高眼睛的众包以（1）动员更多的人类努力，以及（2）更有效地利用人类的努力。项目旨在从根本上改善这两个方面，从而使视网膜连接可以通过Eyewire II实现。在AIM 1中，我们将创建一个引人注目的手机游戏，其目标是使10倍的人比现有的眼睛社区多。在AIM 2中，我们将开发和部署新的众包算法，这些算法通过加权投票并最佳地分配给任务，从人群中提取智慧。目的将通过三个组织之间的合作来实现。有条件不同的是，Inc。（WD）是一个新的位于波士顿的非专业组织，该组织致力于最近从麻省理工学院脱离的“公民神经科学”。 WD目前与普林斯顿神经科学研究所合作运营着眼神。卡内基·梅隆大学（Carnegie Mellon University）的娱乐技术中心（ETC）将为其硕士学生提供基于项目的课程，以设计和原型手机游戏的新想法。这两个目标都将产生将公开可用的代码和算法，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图像很有用。 AIM 2的众包算法对于任何面临的公民科学项目都可能有用，该项目面临着从异质志愿者群体中获得准确可靠的结果的挑战。