Sakana AI trains neural nets without backpropagation

Sakana AI's Error Diffusion method trains dual-stream excitatory/inhibitory networks that obey Dale's principle, a biological constraint real neurons can't satisfy under standard backpropagation. Using a technique called modulo error routing, it reached 96.7% accuracy on MNIST and 61.7% on CIFAR-10 without backpropagation.

Why it matters: Backpropagation-free training has long been pursued as a path to more biologically plausible learning algorithms, potentially enabling different hardware architectures or continual-learning setups. The CIFAR-10 result, though well below backprop-trained baselines, shows the approach scaling past toy problems like MNIST for the first time.