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.

本文强调了人脑的二维进化对于深度神经网络的进化建模的重要性，提出了通过进化算法对深度神经网络进行自动构建的研究前沿，并探讨了 dropout 方法和神经发生与 DNNs 进化之间的联系，为增强深度神经网络的自动构建提出了几项建议。

神经发生、神经达尔文主义和物种进化是否都可以成为深度神经网络演化的灵感来源？

Is it conceivable that neurogenesis, neural Darwinism, and species  evolution could all serve as inspiration for the creation of evolutionary  deep neural networks?

Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms.

本文通过加入新神经元到深度神经网络的层中，探索了连续学习的潜力，并通过 MNIST 手写数字数据集和 NIST SD 19 数据集证明了神经发生有助于解决适应性机器学习算法中的稳定性和可塑性两难问题。