Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing.

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Event-driven contrastive divergence for spiking neuromorphic systems (2013, methodology): “This may be impractical due to the very large number of synapse circuits in the network, but may be less problematic when using Resistive RandomAccess Memorys (RRAMs) (also called memristors) crossbar arrays to emulate synapses [12, 34, 51].”
Synaptic electronics: materials, devices and applications (2013, background): “A fruitful area of research is the development of plasticity schemes for synaptic devices with ns or μs programming times to compensate for the timing discrepancy between the programming pulse width and the STDP window [38, 43], while keeping the synaptic energy consumption as low as possible.”
Memory and Information Processing in Neuromorphic Systems (2015, background): “but that exhibit a range of different behaviors include SpinTransfer Torque Magnetic Random Access Memories (STTMRAMs) [146], [147], [148], ferroelectric devices [149], and phase change materials [150], [151], [152].”
Integration of nanoscale memristor synapses in neuromorphic computing architectures (2013, background): “(Some figures may appear in colour only in the online journal)”
SPINDLE: SPINtronic Deep Learning Engine for large-scale neuromorphic computing (2014, methodology): “In recent years there have been eﬀorts to use emerging technologies such as PCRAM [23], memristors [24], and spin-tronics [10] to realize neurons and synapses.”