  The metabolism of cells is the most basic and important part of human
function. Neural networks in deep learning stem from neuronal activity. It is
self-evident that the significance of metabolize neuronal network(MetaNet) in
model construction. In this study, we explore neuronal metabolism for shallow
network from proliferation and autophagy two aspects. First, we propose
different neuron proliferate methods that constructive the selfgrowing network
in metabolism cycle. Proliferate neurons alleviate resources wasting and
insufficient model learning problem when network initializes more or less
parameters. Then combined with autophagy mechanism in the process of model self
construction to ablate under-expressed neurons. The MetaNet can automatically
determine the number of neurons during training, further, save more resource
consumption. We verify the performance of the proposed methods on datasets:
MNIST, Fashion-MNIST and CIFAR-10.
