Table 1:

Detailed comparison of algorithms.

	BP	FA	DFA	SF	KP	WM	PAL	PFA
No need to transport weight sign	✗	✓	✓	✗	✓	✓	✓	✓
No need to transport weight magnitude	✗	✓	✓	✓	✓	✓	✓	✓
No separate feedback weight learning phase	✓	✓	✓	✛	✓	✗	✓	✓
No explicit weight symmetry after training	✗	✽	✽	✽	✗	✗	✽	✓
Accurate approximation to BP (path alignment)	✓	✗	✗	✗	✓	✓	✗	✓
BP-level task performance	✓	✗	✗	✦	✓	✓	✦	✓

BP: backpropagation. FA: feedback alignment. DFA: direct feedback alignment. SF: sign-concordant feedback. KP: Kollen-Pollack algorithm. WM: weight mirror. PAL: phaseless alignment learning. PFA: product feedback alignment.

^✛: It is unclear how the feedback weights in SF can be learned in a biologically plausible way.

^✽: these algorithms reduce, but do not fully eliminate explicit weight symmetry.

^✦: These algorithms significantly outperform FA and DFA, but still underperform compared to BP in more challenging tasks (CIFAR10 for PAL and ImageNet for SF).