  -In recent years, there has been an increased focus on the mechanics of
information transmission in spiking neural networks. Especially the Noise
Shaping properties of these networks and their similarity to Delta-Sigma
Modulators has received a lot of attention. However, very little of the
research done in this area has focused on the effect the weights in these
networks have on the Noise Shaping properties and on post- processing of the
network output signal. This paper concerns itself with the various modes of
network operation and beneficial as well as detrimental effects which the
systematic generation of network weights can effect. Also, a method for
post-processing of the spiking output signal is introduced, bringing the output
signal more in line with conventional Delta-Sigma Modulators. Relevancy of this
research to industrial application of neural nets as building blocks of
oversampled A/D converters is shown. Also, further points of contention are
listed, which must be thoroughly researched to add to the above mentioned
applicability of spiking neural nets.
