Detecting neuronal activity for rehabilitation assistive devices is an example of extreme edge computing, featuring the system low-power consumption and low lantency. We proposed a neural recording system which detects neural spikes directly on the signals collected from electrophysiological probes. The memristor along the differential path is utilised as trimming device in amplifier, allowing system offset tuning with micron-volt precision. In this paper, we study the impact of memristor IV non-linearity on the performance. The results prove that introducing IV non-linearity does not materially change on overall performance. This was the last conceptual bottleneck identified before practical implementation.