Memristors have emerged as promising, area-efficient, nano-scale devices for implementing models of synaptic plasticity in hybrid CMOS-memristor neuromorphic architectures. These architectures aim at reproducing the learning capabilities of biological networks by emulating the complex dynamics of biological neurons and synapses. However, to maximize the density of these elements in crossbar arrays, learning circuits have often been limited to the implementation of simple spike timing-dependent plasticity (STDP) mechanisms. We propose novel hybrid CMOS-memristor circuits that reproduce more effective and realistic plasticity rules which depend on the timing of the pre-synaptic input spike and on the state of the post-synaptic neuron, and which allow the integration of dense crossbar memristor arrays. To implement these plasticity rules in memristor crossbar arrays, the circuits driving the memristors' post-synaptic terminals actively sense the activity on the pre-synaptic terminals to apply the appropriate stimulation waveforms across the memristors. We illustrate the advantages of this scheme by using it to implement a spike-based perceptron plasticity r ule.