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学术报告预告 (2011-B-05)
作者:          发布时间:2011-10-12     阅读:

报 告 人:北京大学澳门24小时线上娱乐场城app生物信息中心   陶乐天研究员

主办单位:澳门24小时线上娱乐场城app

报告 1 学术规范讲座

报告内容简介

报告中首先讲述什么叫剽窃,并指出剽窃的后果。报告中还将详细分析剽窃的5种类型,分别是“word for word”一字不漏地抄”;“paraphrase” 改字不改意的抄;“plagiarizing ideas”偷别人的观点;“plagiarizing from secondary sources” 剽窃二手文件,即当看的文献是综述性文章,在引用时只引原文而不引综述;以及“self plagiarism”,即一稿多投的自我剽窃现象。最后,报告还将对如何避免剽窃,写好一篇真实、精准、切题的论文,给出几点建议。

报告PPT链接:
http://www.bio.pku.edu.cn/news/student_affairs/2010-05-18.539.html

报告时间:10月14日(星期五) 14:30 - 16 :30

报告地点:六艺楼报告厅

报告2  Mapping Functional Connectivity with Larval Zebrafish Transgenic

for a Ratiometric Calcium Indicator

报告内容摘要: The ability to map functional connectivity is necessary for the study of the flow of activity in neuronal circuits. Optical imaging of calcium indicators, including FRET-based genetically encoded indicators and extrinsic dyes, is an important adjunct to electrophysiology and is widely used to visualize neuronal activity. However, techniques for mapping functional connectivities with calcium imaging data have been lacking. We present a procedure to compute effective functional couplings from ratiometric calcium imaging data in three steps: 1) calculation of calcium concentrations and neuronal firing rates from ratiometric data; 2) identification of putative neuronal populations from spatio-temporal timeseries of neural bursting activity; and then, 3) derivation of effective connectivity matrices that represent neuronal population interactions. We apply our method to the larval zebrafish central nervous system undergoing chemoconvulsant induced seizures. This automatic functional connectivity mapping procedure provides a practical and user-independent means for summarizing the flow of activity through neural pathways.

报告时间:10月15日(星期六) 14:30 - 16 :30

报告地点:格物楼 3201

报告3  Low-Dimensional Characterization of Neuronal Network Activity

in a Large-Scale Model of the Visual Cortex

报告内容摘要: A major theoretical challenge in systems neuroscience modeling is to summarize the dynamics of complex neuronal networks in low dimensional models. While most approaches have focused either on developing reduced descriptions of single neurons or on mean-field, population density models of networks, here, we describe our progress in developing low-dimensional dynamical systems models of large-scale cortical networks using a data-driven approach. Taking a model visual cortical network to be our experimental system, we use empirical principal components analysis (PCA) of simulation data as a dimension reduction tool to generate low-dimensional dynamical systems which allow us to predict (and postdict) the simulation data in an approximate, but mathematically consistent, fashion. Furthermore, we use this empirical, data-driven PCA on a small subset of model neurons; our results suggest that it may be possible to generate such target dynamical systems from simultaneous electro-physiological measurements of network neurons "in vivo".

报告时间:10月17日(星期一) 14:30 - 16 :30

报告地点:格物楼 3201

欢迎广大师生前来参加!

报告人简介

陶乐天

Louis Tao

1986—1990 年哈佛大学物理系学士学位

1990—1995 年芝加哥大学物理系博士学位

1995—1997 年剑桥大学应用数学与理论物理系博士后

1997—2000 年哥伦比亚大学天文系 NSF 博士后奖

2000—2003 年纽约大学科朗研究院副研究员

2003—2007 年新泽西理工学院数学科学系助理教授

2008— 北京大学澳门24小时线上娱乐场城app生物信息中心 研究员

研究方向:

1. 哺乳动物视皮层V1 神经网络的数学模拟;

2. 自动化的神经网络建模与降维系统;

3. 发展针对复杂生物系统的建模与分析工具