A local hebbian rule for deep learning • r/MachineLearning

This hebbian/anti-hebbian rule (see below) efficiently converges deep models in the context of a Reinforcement Learning regime. In a nutshell the rule says if there is no pre-synaptic spike then there will be no weight change (to preserve connections that were not responsible). Otherwise the direction of weight change will depend on whether a post-synaptic spike occured and whether there was a reward. I have not been able to find much existing work re: local rules for deep models, however it's quite likely this rule exists elsewhere..