If platonic representation hypothesis holds across substrates, then it might matter very little, in the end. It holds across architectures in ML, empirically.

The crowd of "backpropagation and Hebbian learning + predictive coding are two facets of the very same gradient descent" also has a surprisingly good track record so far.