Hebbian plasticity could be the brain’s trick to make self-supervised learning work

Please take a look at our new preprint “The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.”


In this work led by Manu Srinath Halvagal we argue that Hebbian plasticity could be the essential ingredient that allows the brain to perform self-supervised learning without the problem of representational collapse. Feedback is welcome, as always!

Our work conceptually links negative-sample-free self-supervised learning that rely on neuronal decorrelation (Barlow Twins) and variance regularization (VICreg) to a Hebbian plasticity model, which shares several notable similarities with, but also generalizes, BCM theory.

We show that in a layer-local learning setting (greedy learning), the plasticity model disentangles object representations in deep neural networks. 

Finally, the same model faithfully captures neuronal selectivity changes of in-vivo unsupervised learning experiments in monkey IT (Li and DiCarlo, 2008).