图 书 馆
用于连通性的深度学习Deep Learning for Connectomicss |
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| 课程网址: | https://videolectures.net/videos/kdd2016_ji_deep_learning |
| 主讲教师: | Shuiwang Ji |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The importance of research that aims to unlock the mystery of the human brain has recently been recognized worldwide. In January 2013, the European Union selected the Human Brain Project to be one of its two flagship projects. In April 2013, the White House announced the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to generate a dynamic map of the brain. As these projects move forward, big data analytics will be playing increasingly important roles in converting big brain data into useful knowledge. A key challenge in analyzing brain data is to construct feature representations from brain images. In this talk, I will discuss our efforts in developing deep computational models for learning representations from brain data. In particular, I will provide details on how to use deep learning methods to elucidate the micro-scale brain connectomics among neurons. I will also show that our methods can be used in a number of diverse computational brain discovery tasks. Additionally, they may be used in other areas beyond brain analytics. |
| 关 键 词: | 连通性; 深度学习; 创新神经技术 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-16：liyq |
| 最后编审: | 2025-03-16：liyq |
| 阅读次数: | 5 |