  Dedicated hardware implementations of spiking neural networks that combine
the advantages of mixed-signal neuromorphic circuits with those of emerging
memory technologies have the potential of enabling ultra-low power pervasive
sensory processing. To endow these systems with additional flexibility and the
ability to learn to solve specific tasks, it is important to develop
appropriate on-chip learning mechanisms.Recently, a new class of three-factor
spike-based learning rules have been proposed that can solve the temporal
credit assignment problem and approximate the error back-propagation algorithm
on complex tasks. However, the efficient implementation of these rules on
hybrid CMOS/memristive architectures is still an open challenge. Here we
present a new neuromorphic building block,called PCM-trace, which exploits the
drift behavior of phase-change materials to implement long lasting eligibility
traces, a critical ingredient of three-factor learning rules. We demonstrate
how the proposed approach improves the area efficiency by >10X compared to
existing solutions and demonstrates a techno-logically plausible learning
algorithm supported by experimental data from device measurements
