SDS Seminar Series – Michele Guindani, University of California, Los Angeles

The Fall 2025 SDS Seminar Series continues on October 10th from 2:00 p.m. to 3:00 p.m. with Dr. Michele Guindani (Professor, Department of Biostatistics, University of California, Los Angeles). This event is in-person in the Avaya Room (POB 2.302).    

Title: Embracing Heterogeneity: Bayesian Clustering Methods for Neuroscience Data

Abstract: A common practice in neuroscience is to average data across analytical units such as neurons, trials, or subjects. While averaging can improve signal, it often masks important heterogeneity in brain activity. In this talk, I will present two recent methods that employ Bayesian clustering to recover latent subpopulations and neuronal ensembles, with applications in human and animal experiments. First, I will introduce a repulsive mixture model applied to Event-Related Potential (ERP) data. ERPs are small voltage changes in the brain's electrical activity, measured by electroencephalography (EEG), that are time-locked to a specific stimulus or cognitive event. Our model assigns a projection determinantal point process (pDPP) prior to the cluster locations, which mathematically enforces separation and helps uncover these hidden neuro-cognitive subgroups. The analytical tractability of the pDPP prior leads to efficient algorithms that yield parsimonious, interpretable partitions with strong frequentist guarantees, including posterior consistency and the automatic elimination of redundant components. Second,  I will describe a Bayesian semiparametric framework for calcium imaging that jointly infers neural spikes and identifies functionally coherent ensembles. We model spiking probability with latent Gaussian processes and promote anatomical coherence using a location-dependent stick-breaking prior. Applied to hippocampal recordings from a mouse navigating a circular arena, the model reveals spatially structured co-activation patterns that vary with the animal's position. Taken together, these studies demonstrate the value of Bayesian clustering techniques for uncovering structure in human ERPs and mouse calcium imaging data.