Memristors are solid-state devices that exhibit voltagecontrolled conductance. This tunable functionality enables the implementation of biologically-inspired synaptic functions in solid-state neuromorphic computing systems. However, while memristors are meant to emulate an intricate signal transduction process performed by soft biomolecular structures, they are commonly constructed from silicon- or polymer-based materials. As a result, the volatility, intricate design, and high-energy resistance switching in memristive devices, usually, leads to energy consumption in memristors that is several orders of magnitude higher than in natural synapses. Additionally, solidstate memristors fail to achieve the coupled dynamics and selectivity of synaptic ion exchange that are believed to be necessary for initiating both short- and long-term potentiation (STP and LTP) in neural synapses, as well as paired-pulse facilitation (PPF) in the presynaptic terminal. LTP is a phenomenon mostly responsible for driving synaptic learning and memory, features that enable signal transduction between neurons to be history-dependent and adaptable. In contrast, current memristive devices rely on engineered external programming parameters to imitate LTP. Because of these.