Gerard Joey Broussard

This paper introduces a Bayesian method using particle Gibbs sampling to infer high-frequency neuronal spikes and quantify their uncertainty, showing improved performance on benchmark datasets.

The paper develops biophysically-inspired models to improve spike inference from calcium imaging by accounting for kinetic features of fluorescent indicators.

The paper develops fast and sensitive GCaMP indicators for neural activity imaging, achieving ultra-fast kinetics and high sensitivity.

SynQuant quantifies synapses from microscopy images by addressing antibody specificity, brightness heterogeneity, and noise, showing improved performance over existing tools in disease models.