In "Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise," published August 19, 2020 in PLOS Computational Biology, researchers including Professor Jonathan Simon develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting.