Fabrizio Musacchioπ§ π Fascinating new study: Pre-training #NeuralNetworks with spontaneous retinal waves β those endogenous activity patterns in the developing eye β significantly improves motion prediction in natural scenes.
May, Dauphin & Gjorgjieva show that even before #vision, the #brain may self-organize using internally generated signals.
π https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012830
π» Code: https://github.com/comp-neural-circuits/pre-training-ANNs-with-retinal-waves
#Neuroscience #AI #ComputationalBiology #SelfOrganization #RetinalWaves #CompNeuro
Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes
Author summary Before the onset of vision, the retina generates its own spontaneous activity, referred to as retinal waves. This activity is crucial for establishing neural connections and, hence, ensuring the proper functionality of the visual system. Recent research has shown that retinal waves exhibit statistical properties similar to those of natural visual stimuli, such as the optic flow of objects in the environment during forward motion. We investigate whether retinal waves can prepare the visual system for motion processing by pre-training artificial neural network (ANN) models with retinal waves. We tested the ANNs on next-frame prediction tasks, where the model predicts the next frame of a video based on previous frames. Our results showed that ANNs pre-trained with retinal waves exhibit faster learning on movies featuring naturalistic stimuli. Additionally, pre-training with retinal waves refined the receptive fields of ANN neurons, similar to processes seen in biological systems. Our work highlights the importance of spatio-temporally patterned spontaneous activity in preparing the visual system for motion processing in natural scenes.
Technische UniversitΓ€t MΓΌnchen
eLife