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.

翻译：深度神经网络（DNNs）基于人工神经网络构建，属于机器学习方法，能够从数据中学习，并已广泛应用于多个领域。DNNs主要依靠人工设计，通常包含大量层级。当前研究前沿已出现通过进化算法自动构建DNNs的方法。本文强调了所谓“二维大脑进化”的重要性，并探讨了其如何启发二维DNN进化建模。我们还重点阐述了广泛用于DNN正则化的丢弃方法与大脑神经发生之间的联系，以及这些概念如何推动DNN进化。最后，本文提出了若干增强DNN自动构建能力的建议。