Hebbian learning is a good biologically plausible learning rule. It works. Minsky's result used a popular but too simple model. Still, that led to back propa

Hebbian learning is a good biologically plausible learning rule. It works. Minsky's result used a popular but too simple model. Still, that led to back propagation which the field has been squeezing as much as it can out of since. Decades ago that result was bypassed by adding a term of location into the network model (i.e. Hopfield+Hebbian) and modulating according to a locationally differentiated trophic factor (i.e. the stuff that the molecular processes of learning use as input). This allows for linearly inseparable functions to be learned (in not really but important "contradiction" to Minsky's result). Jeffrey Elman and others found this in the 90s and I was able to replicate it up to six dimensions in 2004. So we didn't really need back propagation, though it's been useful. Admittedly these models remove even more legibility from the models.