  Learning in the brain is poorly understood and learning rules that respect
biological constraints, yet yield deep hierarchical representations, are still
unknown. Here, we propose a learning rule that takes inspiration from
neuroscience and recent advances in self-supervised deep learning. Learning
minimizes a simple layer-specific loss function and does not need to
back-propagate error signals within or between layers. Instead, weight updates
follow a local, Hebbian, learning rule that only depends on pre- and
post-synaptic neuronal activity, predictive dendritic input and widely
broadcasted modulation factors which are identical for large groups of neurons.
The learning rule applies contrastive predictive learning to a causal,
biological setting using saccades (i.e. rapid shifts in gaze direction). We
find that networks trained with this self-supervised and local rule build deep
hierarchical representations of images, speech and video.

Neural and Evolutionary Computing;Artificial Intelligence;Hardware Architecture;Computer Vision and Pattern Recognition;Machine Learning