  The emergence of nano-scale memristive devices encouraged many different
research areas to exploit their use in multiple applications. One of the
proposed applications was to implement synaptic connections in bio-inspired
neuromorphic systems. Large-scale neuromorphic hardware platforms are being
developed with increasing number of neurons and synapses, having a critical
bottleneck in the online learning capabilities. Spike-timing-dependent
plasticity (STDP) is a widely used learning mechanism inspired by biology which
updates the synaptic weight as a function of the temporal correlation between
pre- and post-synaptic spikes. In this work, we demonstrate experimentally that
binary stochastic STDP learning can be obtained from a memristor when the
appropriate pulses are applied at both sides of the device.
