Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Bernd Illing, Jean Robin Ventura, Guillaume Bellec, Wulfram Gerstner

Keywords: Synaptic plasticity, Hebbian learning, deep learning, self-supervised learning, contrastive predictive coding

TL;DR: We propose a new local learning rule, inspired by neuroscience and self-supervised deep learning, and demonstrate that it can train deep neural networks on images, speech and video.

Abstract: 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.

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Code: https://github.com/EPFL-LCN/pub-illing2021-neurips