On this video
a neurologist starts by saying that we do not know how neurons calculate gradients for backpropagation.
At minute 30:39 hes showing faster convergence for "our algorithm", which seems to converge faster than backpropagation.
After 34:36 it goes explaining how "neurons" in the brain are actually packs of neurons.
I do not really understand all that he says, so I infer that those packs of neurons (which seem depicted as a single layer) are the ones who calculate the gradient. It would make sense if each neuron makes a sightly different calculation, and then each other communicate the difference in results. That would allow to deduce a gradient.
What can be deduced, from the presented information, about the purported "algorithm"?? (From the viewpoint of improving convergence of an artificial neural network).