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GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Nasir Ahmad · Marcel A. J. van Gerven · Luca Ambrogioni

Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Deep Learning
in Orals & Spotlights Track 08: Deep Learning »

Traditional backpropagation of error, though a highly successful algorithm for learning in artificial neural network models, includes features which are biologically implausible for learning in real neural circuits. An alternative called target propagation proposes to solve this implausibility by using a top-down model of neural activity to convert an error at the output of a neural network into layer-wise and plausible ‘targets’ for every unit. These targets can then be used to produce weight updates for network training. However, thus far, target propagation has been heuristically proposed without demonstrable equivalence to backpropagation. Here, we derive an exact correspondence between backpropagation and a modified form of target propagation (GAIT-prop) where the target is a small perturbation of the forward pass. Specifically, backpropagation and GAIT-prop give identical updates when synaptic weight matrices are orthogonal. In a series of simple computer vision experiments, we show near-identical performance between backpropagation and GAIT-prop with a soft orthogonality-inducing regularizer.

Author Information

Nasir Ahmad (Donders Institute for Brain, Cognition and Behaviour, Radboud University)
Marcel A. J. van Gerven (Radboud Universiteit)
Luca Ambrogioni (Radboud University)

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