sure there are other means of training, I was highlighting that nearly all approaches use only one improvement approach during training (gradient descent, genetic algorithms, ...) and if multiple are used the whole model is using the same balance of multiple approaches.

Ah then agreed. My take is if you have a feedforward network, add time delays, because synapses aren't running in lockstep, and add connections arbitrarily with any two neurones, only limited by the physical distance between them, and then add in every kind of diffuse effect of neurotransmitters and all the brain chemicals we haven't discovered yet, and then figure out how to train it... you still won't have a human brain equivalent, because it is harder than that. But you might get something that can learn! All you need is a dopamine reward button, and some way to whack it with a stick every now and then.

I think we're more likely to create AGI than to understand how the brain works in our lifetimes.