Feedback alignment shows fixed random feedback weights can train networks to near-backprop accuracy — but whether it "is" backpropagation is disputed

Feedback alignment (Lillicrap et al., 2016) attacks the weight-transport objection (claim-hinton-biological-implausibility-four-objections, point 1) head-on: replace the transposed forward weights in the backward pass with fixed random feedback weights, and networks still learn to near-backprop test accuracy — including with 50% of feedback connections removed. The forward weights learn to "align" with the random feedback rather than the feedback matching the forward, dissolving the requirement that cortex mirror its own connectivity.

The framing is contested at Tier 1, and the note holds both sides: critics argue FA does not compute or approximate the true loss gradient at all (Chu & Bacho 2023, mathematical non-equivalence) and does not reach parity at scale (Bartunov et al. 2018, empirical). Whether the field now treats either critique as decisive is unsettled and recorded as such — a live dispute, not a verdict. This is the empirical companion to backpropagation-gap's "brains share representations but not mechanism": FA is one concrete candidate for what the different mechanism might be. See moc-backpropagation-origins.