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, some researchers have argued that due to their one integrator structure, spiking neural nets cannot achieve Noise Shaping in excess of first order (20 dB/dec), therefore holding little value with regard to industrial applications. This paper concerns itself with several modifications made to the original Integrate-and-Fire (IF) neuron and their effect on raising the Noise Shaping performance of these nets above the first order barrier. 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 investigated to add to the above mentioned applicability of spiking neural nets.