Geometry reveals an instructive role of retinal waves as biologically plausible pre-training signals

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 retinal waves strongly influence sensory representations before the visual experience. We aim to elucidate the computational role of retinal waves by using them as pre-training signals for neural networks. We consider simulated activity patterns generated by a model retina as well as real activity patterns observed experimentally in a developing mouse retina. We show that pre-training a classifier with a biologically plausible Hebbian learning rule on both simulated and real wave patterns improves the separability of the network’s internal representations. In particular, the pre-trained networks achieve higher classification accuracy and exhibit internal representations with higher manifold capacity when compared to networks with randomly shuffled synaptic weights.