RT Journal Article
SR Electronic
T1 Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.10.242958
DO 10.1101/2020.08.10.242958
A1 Uran, Cem
A1 Peter, Alina
A1 Lazar, Andreea
A1 Barnes, William
A1 Klon-Lipok, Johanna
A1 Shapcott, Katharine A
A1 Roese, Rasmus
A1 Fries, Pascal
A1 Singer, Wolf
A1 Vinck, Martin
YR 2020
UL http://biorxiv.org/content/early/2020/08/10/2020.08.10.242958.abstract
AB Feedforward deep neural networks for object recognition are a promising model of visual processing and can accurately predict firing-rate responses along the ventral stream. Yet, these networks have limitations as models of various aspects of cortical processing related to recurrent connectivity, including neuronal synchronization and the integration of sensory inputs with spatio-temporal context. We trained self-supervised, generative neural networks to predict small regions of natural images based on the spatial context (i.e. inpainting). Using these network predictions, we determined the spatial predictability of visual inputs into (macaque) V1 receptive fields (RFs), and distinguished low- from high-level predictability. Spatial predictability strongly modulated V1 activity, with distinct effects on firing rates and synchronization in gamma-(30-80Hz) and beta-bands (18-30Hz). Furthermore, firing rates, but not synchronization, were accurately predicted by a deep neural network for object recognition. Neural networks trained to specifically predict V1 gamma-band synchronization developed large, grating-like RFs in the deepest layer. These findings suggest complementary roles for firing rates and synchronization in self-supervised learning of natural-image statistics.Competing Interest StatementThe authors have declared no competing interest.