Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely redsignaluced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principal component analysis.