Visual recognition is a fundamental function of the human brain, relying on a cascade of neural processes to transform low level inputs into semantic content and subsequent encoding into memory. Despite significant advances in characterizing the locus and function of key perceptual and mnemonic cortical regions, integrating the temporal and spatial dynamics of this processing stream has been a challenge. In this talk, Mohsenzadeh will present a series of works which address this challenge by showing how the combination of MEG (or EEG), functional MRI measurements, representational geometry and deep neural networks can give new insights into visual processes in the human brain. First, she will present a novel method to characterize the interplay of feedforward and feedback mechanisms along the human ventral visual stream, and suggest how recurrent artificial neural networks can better explain the neural data in challenging visual tasks. Second, she will show how some visual events are privileged by perceptual processing for potential successful memory encoding, offering a new way to characterize the spatiotemporal neural signature of visual memorability in the human brain. Finally, using a novel method to examine what an artificial deep neural network has learned, she will show how biological and artificial networks share many more similarities than previously believed (i.e. topographical similarities, temporal correspondence).