  Several recent studies attempt to address the biological implausibility of
the well-known backpropagation (BP) method. While promising methods such as
feedback alignment, direct feedback alignment, and their variants like
sign-concordant feedback alignment tackle BP's weight transport problem, their
validity remains controversial owing to a set of other unsolved issues. In this
work, we answer the question of whether it is possible to realize random
backpropagation solely based on mechanisms observed in neuroscience. We propose
a hypothetical framework consisting of a new microcircuit architecture and its
supporting Hebbian learning rules. Comprising three types of cells and two
types of synaptic connectivity, the proposed microcircuit architecture computes
and propagates error signals through local feedback connections and supports
the training of multi-layered spiking neural networks with a globally defined
spiking error function. We employ the Hebbian rule operating in local
compartments to update synaptic weights and achieve supervised learning in a
biologically plausible manner. Finally, we interpret the proposed framework
from an optimization point of view and show its equivalence to sign-concordant
feedback alignment. The proposed framework is benchmarked on several datasets
including MNIST and CIFAR10, demonstrating promising BP-comparable accuracy.
