  Many of the recent advances in the field of artificial intelligence have been
fueled by the highly successful backpropagation of error (BP) algorithm, which
efficiently solves the credit assignment problem in artificial neural networks.
However, it is unlikely that BP is implemented in its usual form within
biological neural networks, because of its reliance on non-local information in
propagating error gradients. Since biological neural networks are capable of
highly efficient learning and responses from BP trained models can be related
to neural responses, it seems reasonable that a biologically viable
approximation of BP underlies synaptic plasticity in the brain.
Gradient-adjusted incremental target propagation (GAIT-prop or GP for short)
has recently been derived directly from BP and has been shown to successfully
train networks in a more biologically plausible manner. However, so far, GP has
only been shown to work on relatively low-dimensional problems, such as
handwritten-digit recognition. This work addresses some of the scaling issues
in GP and shows it to perform effective multi-layer credit assignment in deeper
networks and on the much more challenging ImageNet dataset.
