of neurons and synapses is necessary. Moreover, the functions of biological synapses, particularly its capability to permit the transmission of time-dependent signals between neurons, are essential to demonstrate spiking neural networks (SNNs) for parallel operations. [4,5] In this regard, artificial synapses mimicking event-driven synaptic behaviors have been demonstrated using various architectures, including complementary metal-oxidesemiconductor transistors, [6] emerging transistors operated by ferroelectric or electrochemical gate coupling, [7-9] and memristors based on non-volatile memories. [10-12] Among these, memristors have been considered as one of the most promising candidates in neuromorphic computing applications owing to their intrinsic capability to remember the historical information of previously supplied electrical signals even with a single unit device. After Chua et al. theoretically proved the existence of a memristor in 1971, [13,14] tremendous efforts have been devoted to its implementation in an electrical device. After a few decades, in 2008, Williams et al. in Hewlett-Packard Laboratories first reported that memristive behavior can be experimentally achieved in an actual device using the Pt/TiO 2 /Pt structure. [15,16] Their memristive switching behaviors are attributed to the migration of oxygen vacancies in the TiO x layer, triggering the gradient change of the doping state. The active TiO x layer is separated into the undoped TiO 2 and doped TiO 2−x layers as bias above the critical limit is applied. Because TiO 2−x has a variable conductivity depending on its composition, the resistance of the device can be step-wisely changed through the applied bias, which induces the memristive phenomenon. Subsequently, there has been a remarkable progress in memristor-based neuromorphic devices. The capability of various material candidates to mimic essential synaptic functions, including short-term plasticity (STP), long-term plasticity (LTP), short-term depression (STD), long-term depression (LTD), paired pulse facilitation (PPF), and spike-time-dependentplasticity (STDP) learning, has been investigated. [17-19] Each function is an essential element for constructing SNNs. In detail, the STP, STD, LTP, and LTD are kinds of neuroplasticity related to short-term and long-term memory, which represents temporal or long-lasting enhancement/decaying of synaptic connections. PPF is a kind of the short-term plasticity, The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding phys...