Title: Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning,
Abstract: We introduce Error Forward-Propagation, a biologically plausible mechanism to
propagate error feedback forward through the network. Architectural constraints
on connectivity are virtually eliminated for error feedback in the brain;
systematic backward connectivity is not used or needed to deliver error
feedback. Feedback as a means of assigning credit to neurons earlier in the
forward pathway for their contribution to the final output is thought to be
used in learning in the brain. How the brain solves the credit assignment
problem is unclear. In machine learning, error backpropagation is a highly
successful mechanism for credit assignment in deep multilayered networks.
Backpropagation requires symmetric reciprocal connectivity for every neuron.
From a biological perspective, there is no evidence of such an architectural
constraint, which makes backpropagation implausible for learning in the brain.
This architectural constraint is reduced with the use of random feedback
weights. Models using random feedback weights require backward connectivity
patterns for every neuron, but avoid symmetric weights and reciprocal
connections. In this paper, we practically remove this architectural
constraint, requiring only a backward loop connection for effective error
feedback. We propose reusing the forward connections to deliver the error
feedback by feeding the outputs into the input receiving layer. This mechanism,
Error Forward-Propagation, is a plausible basis for how error feedback occurs
deep in the brain independent of and yet in support of the functionality
underlying intricate network architectures. We show experimentally that
recurrent neural networks with two and three hidden layers can be trained using
Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving
$1.90\%$ and $11\%$ generalization errors respectively.