Topic: higher-order visual systems

Modeling neural responses to naturalistic stimuli has been instrumental in advancing our understanding of the visual system. Dominant computational modeling efforts in this direction have been deeply rooted in preconceived hypotheses. Here, we develop a hypothesis-neutral computational methodology which brings neuroscience data directly to bear on the model development process. We demonstrate the effectiveness of this technique in modeling as well as systematically characterizing voxel tuning properties. We leverage the unprecedented scale of the Natural Scenes Dataset to constrain parametriz