Delayed divisive normalisation accounts for a wide range of temporal dynamics of neural responses in human visual cortex

Neural responses in visual cortex exhibit various complex, non-linear temporal dynamics. Even for simple static stimuli, responses decrease when a stimulus is prolonged in time (adaptation), reduce to stimuli that are repeated (repetition suppression), and rise more slowly for low contrast stimuli (slow dynamics). These dynamics also vary depending on the location in the visual hierarchy (e.g., lower vs. higher visual areas) and the type of stimulus (e.g., contrast pattern stimuli vs. real-world object, scenes and face categories). In this talk, I will present two intracranial EEG (iEEG) datasets in which we quantified and modelled the temporal dynamics of neural responses across the visual cortex at millisecond resolution. Our work shows that many aspects of these dynamics are accurately captured by a delayed divisive normalisation model in which neural responses are normalised by recent activation history. I will highlight how fitting this model to the iEEG data unifies multiple disparate temporal phenomena in a single computational framework, thereby revealing systematic differences in temporal dynamics of neural population responses across the human visual hierarchy. Overall, these findings suggest a pervasive role of history-dependent delayed divisive normalisation in shaping neural response dynamics across the cortical visual hierarchy.