  Nanoscale metal oxide memristors have potential in the development of
brain-inspired computing systems that are scalable and efficient1-3. In such
systems, memristors represent the native electronic analogues of the biological
synapses. However, the characteristics of the existing memristors do not fully
support the key requirements of synaptic connections: high density, adjustable
weight, and low energy operation. Here we show a bilayer memristor that is
forming-free, low-voltage (~|0.8V|), energy-efficient (full On/Off switching at
~2pJ), and reliable. Furthermore, pulse measurements reveal the analog nature
of the memristive device, that is it can be directly programmed to intermediate
resistance states. Leveraging this finding, we demonstrate
spike-timing-dependent plasticity (STDP), a spike-based Hebbian learning rule4.
In those experiments, the memristor exhibits a marked change in the normalized
synaptic strength (>30 times) when the pre- and post-synaptic neural spikes
overlap. This demonstration is an important step towards the physical
construction of high density and high connectivity neural networks.
