  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.
