  The backpropagation algorithm is an invaluable tool for training artificial
neural networks; however, because of a weight sharing requirement, it does not
provide a plausible model of brain function. Here, in the context of a
two-layer network, we derive an algorithm for training a neural network which
avoids this problem by not requiring explicit error computation and
backpropagation. Furthermore, our algorithm maps onto a neural network that
bears a remarkable resemblance to the connectivity structure and learning rules
of the cortex. We find that our algorithm empirically performs comparably to
backprop on a number of datasets.
