Abstract for the talk on 11.08.2016 (11:00 h)Seminar on Neural Networks
Andreea Lazar (Ernst Struengmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany)
Efficient encoding of structured stimuli in the primary visual cortex
Early theories of perception have suggested that the brain interprets its visual input on the basis of an internal model of the world. In the early visual system this model is stored on the backbone of a complex recurrent connectivity structure. I will present evidence for efficient recurrent computation in the primary visual cortex based on data obtained with chronic multisite recordings from anesthetized cats and awake monkeys. We focus on stimuli that are less redundant than oriented bars and gratings and thus better suited to capture aspects of distributed coding. We show that the evoked population responses to shapes and natural scenes, exhibit complex temporal dynamics: information about a briefly presented stimulus can persist for up to one second and can superimpose with subsequent stimuli. Structured visual stimuli are encoded more efficiently compared to scrambled controls and engage the local excitatory-inhibitory rhythms in a differential manner. And most remarkably, repetitive visual experience increases the amount of information conveyed by the local population dynamics over time, by consolidating specific low-dimensional representations of stimulus structure.