  Physiological experiments have highlighted how the dendrites of biological
neurons can nonlinearly process distributed synaptic inputs. This is in stark
contrast to units in artificial neural networks that are generally linear apart
from an output nonlinearity. If dendritic trees can be nonlinear, biological
neurons may have far more computational power than their artificial
counterparts. Here we use a simple model where the dendrite is implemented as a
sequence of thresholded linear units. We find that such dendrites can readily
solve machine learning problems, such as MNIST or CIFAR-10, and that they
benefit from having the same input onto several branches of the dendritic tree.
This dendrite model is a special case of sparse network. This work suggests
that popular neuron models may severely underestimate the computational power
enabled by the biological fact of nonlinear dendrites and multiple synapses per
pair of neurons. The next generation of artificial neural networks may
significantly benefit from these biologically inspired dendritic architectures.
