小利兰·斯坦福大学(Leland Stanford Junior University)

常直接称为斯坦福大学(Stanford University),为一所坐落于美国加利福尼亚州斯坦福的私立研究型大学,因其学术声誉和创业氛围而获评为世界上最知名的高等学府之一。 自上世纪七十年代,斯坦福成为了美国SLAC国家加速器实验室的所在地,及其中一个高等研究计划署网络(互联网雏形)的起源地。 学校的校园位于硅谷的西北方,邻近帕罗奥图。斯坦福为一所拥有高住宿率及高选择性的大学,当中的研究生课程较本科的多元化。该校也是马丁路德金手写原稿的保存地。 斯坦福培养了不少著名人士。其校友涵盖30名富豪企业家及17名太空员,亦为培养最多美国国会成员的院校之一。斯坦福校友创办了众多著名的公司机构,如:谷歌、雅虎、惠普、耐克、昇阳电脑等,这些企业的资金合计相等于全球第十大经济体系。共81名诺贝尔奖得主现或曾于该校学习或工作。

斯坦福大学-生物医学科研

一、课题方向

Biomedical Engineering and Sciences

生物医药工程

Biological Sciences

生物医药科学

Applied Physics

应用物理

Neuroscience

神经科学

Biophysics

生物物理学

Molecular Imaging

分子成像

 

二、导师背景

生物学、应用物理学副教授

 

三、科研内容参考

Population analysis of memory-related neural ensemble Keywords: Neural Ensemble, Memory, Calcium Imaging, Decoding

Summary

The system neuroscience field has transitioned from single cell recordings to high through-put monitoring of hundreds or even thousands of neurons simultaneously. The interpretation of these large data sets requires deep understanding of the psychological nature of related behavior, as well as the rigorous application of relevant statistical techniques. In this project, we will explore a large data set of cortical neuronal activity recorded during mice performing a memory-guided task by calcium imaging. We will use population vector, mutual information, and some machine learning techniques to identify memory-related activity and decode the animals behavior. We will design analyses pipelines and try to distinguish neural signature of different psychological constructs.

Students will learn

-- How to open, browse, and process large (individual file over 100Gb) image files.
-- How to use shuffled data to generate null hypothesis to test signal specificity.
-- How to use population vector to describe the real-time ensemble activity in various task-relevant axis.
-- How to construct, cross-validate and test decoders to interpret neural ensemble activity.
-- How to design biological experiments to test the causality suggested by analyses.

Pre-requisite
-- Neuroscience:
Students should understand the classic rodent behavioral tasks for short-term and long-term memory, such as contextual or cued fear conditioning and delayed discrimination.
-- Statistics: Students should understand the principles of hypothesis testing and being able to articulate the interpretation of a statistical test results. Students are expected to be familiar with common parametric and non-parametric tests.
-- Programming: Students should be able to understand basic Matlab scripts.

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