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We introduce a novel energy-efficient methodology “2-PCM Synapse” to use phase-change memory (PCM) as synapses in large-scale neuromorphic systems. Our spiking neural network architecture exploits the gradual crystallization behavior of PCM devices for emulating both synaptic potentiation and synaptic depression. Unlike earlier attempts to implement a biological-like spike-timing-dependent plasticity learning rule with PCM, we use a simplified rule where long-term potentiation and long-term depression can both be produced with a single invariant crystallizing pulse. Our architecture is simulated on a special purpose event-based simulator, using a behavioral model for the PCM devices validated with electrical characterization. The system, comprising about 2 million synapses, directly learns from event-based dynamic vision sensors. When tested with real-life data, it is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway. Complete trajectories can be learned with a detection rate above 90 %. The synaptic programming power consumption of the system during learning is estimated and could be as low as 100 nW for scaled down PCM technology. Robustness to device variability is also evidenced.