Jonathan Pillow's research interests lie at the intersection of computational neuroscience, machine learning, and human visual perception. His lab employs a variety of theoretical tools, in conjunction with psychophysical experiments, to study how neural populations represent and process information. He collaborates closely with labs devoted to neurophysiology and fMRI, applying Bayesian statistical methods to model the responses of neural populations in the visual pathway. Current research topics include: neural decoding methods, neural population coding, psychophysics and modeling of human motion perception, theoretical models of adaptation, natural scene statistics, and supervised and unsupervised learning with spike trains.