  Diverse subfields of neuroscience have enriched artificial intelligence for
many decades. With recent advances in machine learning and artificial neural
networks, many neuroscientists are partnering with AI researchers and machine
learning experts to analyze data and construct models. This paper attempts to
demonstrate the value of such collaborations by providing examples of how
insights derived from neuroscience research are helping to develop new machine
learning algorithms and artificial neural network architectures. We survey the
relevant neuroscience necessary to appreciate these insights and then describe
how we can translate our current understanding of the relevant neurobiology
into algorithmic techniques and architectural designs. Finally, we characterize
some of the major challenges facing current AI technology and suggest avenues
for overcoming these challenges that draw upon research in developmental and
comparative cognitive neuroscience.
