Metaplasticity, a higher order of synaptic plasticity, as well as a key issue in neuroscience, is realized with artificial synapses based on a WO3 thin film, and the activity-dependent metaplastic responses of the artificial synapses, such as spike-timing-dependent plasticity, are systematically investigated. This work has significant implications in neuromorphic computation.

The synaptic weight modification depends not only on interval of the pre-/ postspike pairs according to spike-timing dependent plasticity (classical pair-STDP), but also on the timing of the preceding spike (triplet-STDP). Triplet-STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short-term suppression effect of preceding spikes. Second-order memristors with one state variable possessing short-term dynamics work in a way similar to the biological system. In this work, the suppression triplet-STDP learning rule is faithfully demonstrated by experiments and simulations using second-order memristors. Furthermore, a leaky-integrate-and-fire (LIF) neuron is simulated using a circuit constructed with second-order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet-STDP rule, are realized. The realized weight-dependent pairand triplet-STDP rules are clearly in line with findings in biology. The physically realized triplet-STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second-order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain-inspired computation systems.

Pavlovian conditioning, a classical case of associative learning in a biological brain, is demonstrated using the Ni/Nb-SrTiO3/Ti memristive device with intrinsic forgetting properties in the framework of the asymmetric spike-timing-dependent plasticity of synapses. Three basic features of the Pavlovian conditioning, namely, acquisition, extinction and recovery, are implemented in detail. The effects of the temporal relation between conditioned and unconditioned stimuli as well as the time interval between individual training trials on the Pavlovian conditioning are investigated. The resulting change of the response strength, the number of training trials necessary for acquisition and the number of extinction trials are illustrated. This work clearly demonstrates the hardware implementation of the brain function of the associative learning.

To implement the complex brain functions of learning, forgetting and memory in a single electronic device is very advantageous for realizing artificial intelligence. As a proof of concept, memristive devices with a simple structure of Ni/Nb-SrTiO/Ti were investigated in this work. The functions of learning, forgetting and memory were successfully mimicked using the memristive devices, and the "time-saving" effect of implicit memory was also demonstrated. The physics behind the brain functions is simply the modulation of the Schottky barrier at the Ni/SrTiO interface. The realization of various psychological functions in a single device simplifies the construction of the artificial neural network and facilitates the advent of artificial intelligence.

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