Random synaptic feedback weights support error backpropagation for deep learning.
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 across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.
Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK.|Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Timothy P
TP
Lillicrap
School of Biology, University of St Andrews, Harold Mitchel Building, St Andrews, Fife KY16 9TH, UK.
Daniel
D
Cownden
Departments of Physiology and Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada.|Centre for Vision Research, York University, Toronto, Ontario M3J 1P3, Canada.
Douglas B
DB
Tweed
Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK.
Colin J
CJ
Akerman
D000465:Algorithms; D005246:Feedback; D000069550:Machine Learning; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics