  The backpropagation of error algorithm (BP) is impossible to implement in a
real brain. The recent success of deep networks in machine learning and AI,
however, has inspired proposals for understanding how the brain might learn
across multiple layers, and hence how it might approximate BP. As of yet, none
of these proposals have been rigorously evaluated on tasks where BP-guided deep
learning has proved critical, or in architectures more structured than simple
fully-connected networks. Here we present results on scaling up biologically
motivated models of deep learning on datasets which need deep networks with
appropriate architectures to achieve good performance. We present results on
the MNIST, CIFAR-10, and ImageNet datasets and explore variants of
target-propagation (TP) and feedback alignment (FA) algorithms, and explore
performance in both fully- and locally-connected architectures. We also
introduce weight-transport-free variants of difference target propagation (DTP)
modified to remove backpropagation from the penultimate layer. Many of these
algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP
and FA variants perform significantly worse than BP, especially for networks
composed of locally connected units, opening questions about whether new
architectures and algorithms are required to scale these approaches. Our
results and implementation details help establish baselines for biologically
motivated deep learning schemes going forward.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing;Machine Learning