Why does the brain probably not learn by backpropagation as artificial networks do? · Grey Matter

Backpropagation sends an exact error backwards through the same connections used in the forward pass, which neurons cannot do directly: synapses transmit one way, so the backward path would need separate connections mirroring every forward weight.

Why does the brain probably not learn by backpropagation as artificial networks do?

Backpropagation sends an exact error backwards through the same connections used in the forward pass, which neurons cannot do directly: synapses transmit one way, so the backward path would need separate connections mirroring every forward weight. It also needs graded error values and the derivative of each unit's response, separate phases for computing and for learning, and a controller alternating them, while neurons send discrete spikes and learn continuously as they act. Most researchers conclude that the brain uses local rules that approximate some of its effect, helped by feedback connections and neuromodulators, and the finding that error sent through random feedback weights still trains networks shows the symmetry requirement can be relaxed.