Short-term synaptic plasticity (STP) is a mechanism identified in brain systems according to which the effective connection strength (synaptic strength) between two neurons varies dynamically with recent communication history. As a consequence, the amplitude of the post-synaptic potential in response to a single pre-synaptic event, so-called 'spike', may increase (short-term facilitation) or decrease (short-term depression) with consecutive presynaptic stimulation. However, in contrast to Long-term Synaptic plasticity, these changes are temporary and are typically restored in the absence of input. Interestingly, however, a single neuron which receives input via both facilitating and depressing synapses has improved discrimination capability, distinguishing, for instance, between a sequence of events and a sequence of the same events presented in the reversed order. We, therefore, studied the memory mechanisms in emerging non-CMOS devices with a view to application in temporal pattern recognition and detection, inspired by the STP mechanisms. In particular, we demonstrate that memristors can exhibit a resembling behavior to STP due to an inherent volatility and hysteresis. When stimulated by closely spaced pulse waves, the conductance of the device decreases similar to what a depressing synapse would do if presented with consecutive pre-synaptic spikes. This work paves the way for employing memristors in solving spatio-temporal sequence learning problems.