A0814
Title: Simultaneous estimation and clustering of additive shape invariant models for neural data
Authors: Shizhe Chen - UC Davis (United States)
Zitong Zhang - University of California Davis (United States)
Shizhe Chen - University of California, Davis (United States) [presenting]
Abstract: Technological advancements have enabled the recording of spiking activities from large neuron ensembles, presenting an exciting yet challenging opportunity for statistical analysis. The focus is on 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 firing latencies across neurons. A novel additive shape invariant model is introduced, capable of simultaneously accommodating multiple clusters, additive components, and unknown time shifts. Conditions for the identifiability of model parameters are established, offering guidance for the design of future experiments. The properties of the proposed algorithm are examined through simulation studies, and the proposed method is applied to neural data collected in mice.
