讲座1：
讲座主题：A Novel TCM-based AI Large Model Framework toward Human diseases and Drug-Diseases Associations
讲座时间:2023年10月30日星期一 9:00
讲座所在:沙河校区理学楼202
主 讲 人:田亮 香港浸会大学物理系
摘要:
Traditional Chinese Medicine (TCM), which originated in ancient China with a history of thousands of years, characterizes and addresses human physiology, pathology, and diseases diagnosis and prevention using TCM theories and Chinese herbal products. Recently, the World Health Organization included TCM in the global diagnostic compendium, which marks the international recognition of TCM in global health care. Considering this, many research works have been devoted to revealing the effectiveness and efficacy of Chinese herbs for new drug discovery in a bottom-up manner. However, the pharmacological principles in TCM theory, the core treasure house of TCM, have rarely been systematically investigated in a top-down manner, which hinders the modernization and standardization of TCM. To bridge the gap, we propose a novel TCM-based artificial intelligence (AI) framework to unravel general patterns and principles of human disease and investigate potential drug-diseases associations. We collect and refine extensive TCM data, as well as biological, chemical, and clinical data, to establish an integrated multi-modal TCM database. Subsequently, we construct a TCM pharmacological network to reveals the underlying structure and patterns within the TCM data. An attention-based AI model is trained to embed multi-modal TCM data into an interpretable pharmacological space, allowing for quantitative and personalized analysis of complex interactions among diseases, symptoms, herbs, compounds, and genes. The pharmacological embedding space with biological significance provides new perspectives toward modern medicine issues from the view of TCM.Our work aims to promote the quantitative underpinning of TCM pharmacological principles, provide a basis for the objectification of the diagnosis and treatment process of TCM, and pave the way for the knowledge fusion of TCM evidence-based medicine and modern biology.
报告人简介:
田亮博士现为香港浸会大学（HKBU）物理系助理教授 ，并兼任香港浸会大学情形与生物剖析国家重点实验室和盘算与理论研究所的学术成员 ，以及深圳京鲁盘算科学与应用研究院成员。在加入香港浸会大学之前 ，他曾先后在哈佛大学医学院担当博士后研究员 ，在美国波士顿布莱根妇女医院查宁网络医学部担当研究员。田博士的团队使用种种剖析、数值、建模、模拟、数据挖掘和机械学习手艺 ，对重大系统、统计物理和生物物理学举行前沿的跨学科研究。主要研究偏向包括人类微生物组与群落生态学、生物大数据与机械学习、重大网络：结构与动力学、中医数据挖掘、盛行病学建模等。
讲座2：
讲座主题：时序信息加工的神经盘算机制及其类脑算法研究
讲座时间:2023年10月30日星期一 10:00
讲座所在:沙河校区理学楼202
主 讲 人:弭元元 清华大学心理学系
摘要:
Temporal sequence processing is fundamental in brain cognitive functions. Experimental data has indicated that the representations of ordinal information and contents of temporal sequences are disentangled in the brain, but the neural mechanism underlying this disentanglement remains largely unclear. We investigate how recurrent neural circuits learn to represent the abstract order structure of temporal sequences, and how the disentangled representation of order structure facilitates the processing of temporal sequences. We show that with an appropriate training protocol, a recurrent neural circuit can learn tree-structured attractor dynamics to encode the corresponding tree-structured orders of temporal sequences. This abstract temporal order template can then be bound with different contents, allowing for flexible and robust temporal sequence processing. Using a transfer learning task, we demonstrate that the reuse of a temporal order template facilitates the acquisition of new temporal sequences, if these sequences share the same or partial ordinal structure. Using a key-word spotting task, we demonstrate that the tree-structured attractor dynamics improves the robustness of temporal sequence discrimination, if the ordinal information is the key to differentiate these sequences.
报告人简介:
弭元元 ，清华大学心理学系副教授 ，研究偏向为盘算神经科学。专注于研究脑在网络层面处置惩罚动态信息的一样平常性原理 ，包括事情影象的容量与调控、时空信息的网络编码等；基于此生长了类脑运动模式的快速识别算法、运动目的的展望追踪算法等 ，并与工业界相助探索这些类脑算法在现实场景中的应用。以第一或者通讯（含配合）在神经科学顶级期刊Neuron, PNAS, Progress in Neurobiology ，人工智能顶会NeurIPS等揭晓论文20余篇�；竦霉易匀豢蒲Щ鹞恢Р坑乓烨嗄昊鸷捅本┦锌萍夹滦峭氲认钅康闹С�。
