  Deep learning has led to significant advances in artificial intelligence, in
part, by adopting strategies motivated by neurophysiology. However, it is
unclear whether deep learning could occur in the real brain. Here, we show that
a deep learning algorithm that utilizes multi-compartment neurons might help us
to understand how the brain optimizes cost functions. Like neocortical
pyramidal neurons, neurons in our model receive sensory information and
higher-order feedback in electrotonically segregated compartments. Thanks to
this segregation, the neurons in different layers of the network can coordinate
synaptic weight updates. As a result, the network can learn to categorize
images better than a single layer network. Furthermore, we show that our
algorithm takes advantage of multilayer architectures to identify useful
representations---the hallmark of deep learning. This work demonstrates that
deep learning can be achieved using segregated dendritic compartments, which
may help to explain the dendritic morphology of neocortical pyramidal neurons.
