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Many of the recent advances in the field of artificial intelligence have beenfueled by the highly successful backpropagation of error (BP) algorithm, whichefficiently solves the credit assignment problem in artificial neural networks.However, it is unlikely that BP is implemented in its usual form withinbiological neural networks, because of its reliance on non-local information inpropagating error gradients. Since biological neural networks are capable ofhighly efficient learning and responses from BP trained models can be relatedto neural responses, it seems reasonable that a biologically viableapproximation 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 successfullytrain networks in a more biologically plausible manner. However, so far, GP hasonly been shown to work on relatively low-dimensional problems, such ashandwritten-digit recognition. This work addresses some of the scaling issuesin GP and shows it to perform effective multi-layer credit assignment in deepernetworks and on the much more challenging ImageNet dataset.

Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks