Learning cortical representations through perturbed and adversarial dreaming.

ABSTRACT: Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, non-rapid eye movement (NREM), and REM sleep, optimizing different, but complementary, objective functions. We train the model on standard datasets of natural images and evaluate the qualit

SUBMITTER: Deperrois N 

PROVIDER: S-EPMC9071267 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature