Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks

Understand

Error backpropagation is an extremely effective algorithm for assigning credit in artificial neural networks.

• However, weight updates under Backprop depend on lengthy recursive computations and require separate output and error messages -- features not shared by biological neurons, that are perhaps unnecessary.
• In this paper, we revisit Backprop and the credit assignment problem.
• We first decompose Backprop into a collection of interacting learning algorithms; provide regret bounds on the performance of these sub-algorithms; and factorize Backprop's error signals.