Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry.

ABSTRACT: Prior to the onset of vision, neurons in the developing mammalian retina spontaneously fire in correlated activity patterns known as retinal waves. Experimental evidence suggests that retinal waves strongly influence the emergence of sensory representations before visual experience. We aim to model this early stage of functional development by using movies of neurally active developing retinas as pre-training data for neural networks. Specifically, we pre-train a ResNet-18 with an unsupervised contrastive learning objective (SimCLR) on both simulated and experimentally-obtained movies of retinal waves, then evaluate its performance on image classification tasks. We find that pre-training on retinal waves significantly improves performance on tasks that test object invariance to spatial tra

SUBMITTER: Ligeralde A 

PROVIDER: S-EPMC10723543 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature