  Current algorithms for deep learning probably cannot run in the brain because
they rely on weight transport, where forward-path neurons transmit their
synaptic weights to a feedback path, in a way that is likely impossible
biologically. An algorithm called feedback alignment achieves deep learning
without weight transport by using random feedback weights, but it performs
poorly on hard visual-recognition tasks. Here we describe two mechanisms - a
neural circuit called a weight mirror and a modification of an algorithm
proposed by Kolen and Pollack in 1994 - both of which let the feedback path
learn appropriate synaptic weights quickly and accurately even in large
networks, without weight transport or complex wiring.Tested on the ImageNet
visual-recognition task, these mechanisms outperform both feedback alignment
and the newer sign-symmetry method, and nearly match backprop, the standard
algorithm of deep learning, which uses weight transport.
