  The brain processes information through many layers of neurons. This deep
architecture is representationally powerful, but it complicates learning by
making it hard to identify the responsible neurons when a mistake is made. In
machine learning, the backpropagation algorithm assigns blame to a neuron by
computing exactly how it contributed to an error. To do this, it multiplies
error signals by matrices consisting of all the synaptic weights on the
neuron's axon and farther downstream. This operation requires a precisely
choreographed transport of synaptic weight information, which is thought to be
impossible in the brain. Here we present a surprisingly simple algorithm for
deep learning, which assigns blame by multiplying error signals by random
synaptic weights. We show that a network can learn to extract useful
information from signals sent through these random feedback connections. In
essence, the network learns to learn. We demonstrate that this new mechanism
performs as quickly and accurately as backpropagation on a variety of problems
and describe the principles which underlie its function. Our demonstration
provides a plausible basis for how a neuron can be adapted using error signals
generated at distal locations in the brain, and thus dispels long-held
assumptions about the algorithmic constraints on learning in neural circuits.
