  Deep Neural Networks (DNNs) are built using artificial neural networks. They
are part of machine learning methods that are capable of learning from data
that have been used in a wide range of applications. DNNs are mainly
handcrafted and they usually contain numerous layers. Research frontier has
emerged that concerns automated construction of DNNs via evolutionary
algorithms. This paper emphasizes the importance of what we call
two-dimensional brain evolution and how it can inspire two dimensional DNN
evolutionary modeling. We also highlight the connection between the dropout
method which is widely-used in regularizing DNNs and neurogenesis of the brain,
and how these concepts could benefit DNNs evolution.The paper concludes with
several recommendations for enhancing the automatic construction of DNNs.
