Biomedical Engineering and Sciences
Population analysis of memory-related neural ensemble Keywords: Neural Ensemble, Memory, Calcium Imaging, Decoding
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 animal’s 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.
-- 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.