Random synaptic feedback weights support error backpropagation for deep learning.

ABSTRACT: The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals ac

SUBMITTER: Lillicrap TP 

PROVIDER: S-EPMC5105169 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature