  It has been well-established that within conventional neural networks, many
of the values produced at each layer are zero. In this work, I demonstrate that
spiking neural networks can prevent the transmission of spikes representing
values close to zero using local information. This can reduce the amount of
energy required for communication and computation in these networks while
preserving accuracy. Additionally, this demonstrates a novel application of
biologically observed spiking rhythms.
