生物启发的机器学习：程序化死亡和复制
Bio-inspired Machine Learning: programmed death and replication
摘要：
我们在机器学习的背景下分析生物现象的算法和计算方面，例如复制和程序性死亡。我们使用两种不同的神经元效率度量来开发机器学习算法，用于向系统添加神经元（即复制算法）和从系统中删除神经元（即程序性死亡算法）。我们认为，程序死亡算法可以用于压缩的神经网络和复制算法可以用于提高性能的已经训练好的神经网络。我们还表明，程序死亡和复制的组合算法可以提高任意机器学习系统的学习效率。生物启发算法的计算优势，证明了通过训练前馈神经网络的MNIST数据集的手写图像。
英文摘要：
We analyze algorithmic and computational aspects of biological phenomena, such as replication and programmed death, in the context of machine learning. We use two different measures of neuron efficiency to develop machine learning algorithms for adding neurons to the system (i.e. replication algorithm) and removing neurons from the system (i.e. programmed death algorithm). We argue that the programmed death algorithm can be used for compression of neural networks and the replication algorithm can be used for improving performance of the already trained neural networks. We also show that a combined algorithm of programmed death and replication can improve the learning efficiency of arbitrary machine learning systems. The computational advantages of the bio-inspired algorithms are demonstrated by training feedforward neural networks on the MNIST dataset of handwritten images.
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