  Models of sensory processing and learning in the cortex need to efficiently
assign credit to synapses in all areas. In deep learning, a known solution is
error backpropagation, which however requires biologically implausible weight
transport from feed-forward to feedback paths.
  We introduce Phaseless Alignment Learning (PAL), a bio-plausible method to
learn efficient feedback weights in layered cortical hierarchies. This is
achieved by exploiting the noise naturally found in biophysical systems as an
additional carrier of information. In our dynamical system, all weights are
learned simultaneously with always-on plasticity and using only information
locally available to the synapses. Our method is completely phase-free (no
forward and backward passes or phased learning) and allows for efficient error
propagation across multi-layer cortical hierarchies, while maintaining
biologically plausible signal transport and learning.
  Our method is applicable to a wide class of models and improves on previously
known biologically plausible ways of credit assignment: compared to random
synaptic feedback, it can solve complex tasks with less neurons and learn more
useful latent representations. We demonstrate this on various classification
tasks using a cortical microcircuit model with prospective coding.
