This work explores the architectural trade-offs and implications of a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless iBMI. We use quantitative metrics such as root-mean-square error and correlation coefficient between the original and recovered signal to assess the effect of neuromorphic compression on spike shape, and spike detection accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks. We demonstrate that a data compression ratio of >50 can be achieved by selective transmission of event pulses generated in different modes for large electrode arrays with a correlation coefficient of ~0.9 and a spike detection accuracy of over 90%.