As an emerging technology, memristive devices have flourished to develop energy-efficient computing systems in the form of emerging NonVolatile Memories (eNVMs), synaptic connections, and logic design, which could play a critical role in microelectronic design. In neuromorphic applications, the power-off state of a nonvolatile memristor is crucial, as it determines the learning and forgetting of embedded synaptic efficacy. Due to the complexity of the transport mechanisms contributing to the memristor current, memristor modeling remains a critical challenge. The memductance of memristors includes both volatile and nonvolatile components, which can be modeled using a Memductance Components Adaptive Model (MCAM). The MCAM enables researchers to model the memductance of memristors, which can be used in neuromorphic applications and logic design. Mathematical explanations and two physical models of memristors extracted from experimental results demonstrate the model’s accuracy. The application of MCAM for synaptic updates in spiking neuromorphic systems is also shown.