This thesis investigates the challenge of memory consolidation and learning in artificial synapses. The adoption and evolution of artificial intelligence (AI) also by products a frequently overlooked exponentially increasing need for information processing and data storage. This issue is either met with the physical expansion of storage facilities or with the inevitable forgetting of old information in favour of new; both of which seriously hinder the performance of embedded AI systems. This work presents a novel approach in emulating the complex biochemical mechanisms which allow neuronal synapses to store multiple memories on top of the other and at different timescales, like a palimpsest, and which give rise to the incredible learning capacity of biological intelligence. This work mainly focused on exploiting the intrinsic time dependent volatility in emerging memristive nanotechnologies to showcase palimpsest consolidation. Memristive volatility was studied using a data-driven approach and device-agnostic characterisation and mathematical modelling methods were developed to uncover the main properties of the mechanism. It was found that volatility can exist bidirectionally in TiO2 memristors and that its time constants can be manipulated via the invasiveness and/or frequency of device stimulation. Importantly, within a given observation time window, volatility was shown to operate at two timescales; a fast decay of large magnitude followed by a saturating steady state and a small non-volatile residue. By operating memristive devices as binary synapses, spiking plasticity events were able to store long-term memories in the non-volatile residue, while expressing the opposing state in the short-term. Palimpsest consolidation was examined in simulated memory networks which were able to protect long-term memories while expressing up to hundreds of uncorrelated short-term memories. It was also found that these networks bear close resemblance to the visual working memory of mammalian brains. The same plasticity dynamics were finally extended towards the context of neuronal activity detection, where memristive sensors were able to ’learn’ during high spiking frequencies and ’forget’ during less active timeframes. The results presented in this thesis verify the candidacy of volatile memristors as natural facilitators of learning in AI. The ability to learn continuously without catastrophically forgetting old memories, can create new possibilities in the way AI can be used to undertake more generalised tasks. Moreover, the same artificial synapses have shown immense potential in neural interfacing. This can potentially reshape the ways AI is currently interpreted and lead to novel research which aims to integrate both biological and artificial intelligence.