Simultaneous clustering and estimation of additive shape invariant models for recurrent event data

：陈施喆（美国University of California, Davis）
：2025-12-25 16:30
：海韵园实验楼S307

报告人：陈施喆（美国University of California, Davis）

时  间：2025年12月25日16:30

地  点：海韵园实验楼S307

内容摘要:

Technological advancements have enabled the recording of spiking activities from large neuron ensembles, presenting  an exciting yet  challenging opportunity for statistical analysis. This project considers the challenges from a common type of neuroscience experiments, where randomized interventions are applied over the course of each trial. The objective is to identify groups of neurons with unique stimulation responses and estimate these responses. The observed data, however, comprise superpositions of neural responses to all stimuli, which is further complicated by varying response latencies across neurons. We introduce a novel additive shape invariant model that is capable of simultaneously accommodating multiple clusters, additive components, and unknown time-shifts. We establish conditions for the identifiability of model parameters, offering guidance for the design of future experiments. We examine the properties of the proposed algorithm through simulation studies, and apply the proposed method on neural data collected in mice. Specifically, we analyze neural spike trains recorded using Neuropixels probes during a visual discrimination task with two independent randomized stimuli. The analysis identifies three distinct functional groups of neurons that exhibit heterogeneous response patterns to the stimuli, including both stimulus-specific activity and variability in response timing.

个人简介：

陈施喆，现任加州大学戴维斯分校（UC Davis）统计学系副教授。加入该系之前，曾在哥伦比亚大学担任博士后研究员。他在华盛顿大学攻读生物统计学博士学位，导师为 Ali Shojaie 和 Daniela Witten。他的研究兴趣广泛，主要关注如何从海量数据中学习和刻画大型复杂生物系统所涌现的统计问题，并通过高维统计与图模型方面的统计理论与方法来加以解决。已经在JASA, Biometrika, AOAS等期刊发表论文多篇。

 

联系人：胡杰