  This article highlights specific features of biological neurons and their
dendritic trees, whose adoption may help advance artificial neural networks
used in various machine learning applications. Advancements could take the form
of increased computational capabilities and/or reduced power consumption.
Proposed features include dendritic anatomy, dendritic nonlinearities, and
compartmentalized plasticity rules, all of which shape learning and information
processing in biological networks. We discuss the computational benefits
provided by these features in biological neurons and suggest ways to adopt them
in artificial neurons in order to exploit the respective benefits in machine
learning.
