A ferroelectric memristor is a device that exhibits memristive behavior, where its resistance changes in response to voltage pulses. This study demonstrates that voltage-controlled domain configurations in ferroelectric tunnel barriers can produce memristive behavior with resistance variations exceeding two orders of magnitude and a 10 ns operation speed. The resistance variations are explained by ferroelectric domain dynamics during polarization reversal, and a simple analytical expression for the memristive effect is derived. The results suggest new opportunities for ferroelectrics as the hardware basis of future neuromorphic computational architectures. The study shows that the domain configuration of a ferroelectric tunnel barrier can be used to produce a virtually continuous range of resistance levels between OFF and ON states. Piezoresponse force microscopy (PFM) images and electrical transport measurements are used to analyze the resistance variations. The resistance switching is modeled in terms of domain nucleation and propagation, and the results show that the resistance level of the ferroelectric tunnel junction (FTJ) can be set by one pulse of appropriate amplitude or by an appropriate number of consecutive pulses of a fixed voltage. The study also presents a model for the dynamics of resistance switching, which accounts for the observed "wavy" behavior. The model divides the pad area into zones with different propagation and nucleation kinetics, each ruled by the Kolmogorov-Avrami-Ishibashi (KAI) model. The model provides an analytical expression for the memristive response based on the physical description of the ferroelectric domain dynamics. The results demonstrate that the resistance can be continuously and reversibly tuned over more than two orders of magnitude by varying the pulse amplitude and/or the pulse number. These features qualify FTJs as memristive devices, which is an improvement over previous memristors with a purely electronic mechanism where the resistance contrast is no better than a factor of two. The study also shows that the resistance switching behavior can be modeled using a simple model of domain nucleation and growth in a heterogeneous medium. The results highlight the advantage of using well-established physical phenomena like ferroelectricity in the design of novel memristive systems. The findings open new perspectives for ferroelectrics in next-generation neuromorphic computational architectures.A ferroelectric memristor is a device that exhibits memristive behavior, where its resistance changes in response to voltage pulses. This study demonstrates that voltage-controlled domain configurations in ferroelectric tunnel barriers can produce memristive behavior with resistance variations exceeding two orders of magnitude and a 10 ns operation speed. The resistance variations are explained by ferroelectric domain dynamics during polarization reversal, and a simple analytical expression for the memristive effect is derived. The results suggest new opportunities for ferroelectrics as the hardware basis of future neuromorphic computational architectures. The study shows that the domain configuration of a ferroelectric tunnel barrier can be used to produce a virtually continuous range of resistance levels between OFF and ON states. Piezoresponse force microscopy (PFM) images and electrical transport measurements are used to analyze the resistance variations. The resistance switching is modeled in terms of domain nucleation and propagation, and the results show that the resistance level of the ferroelectric tunnel junction (FTJ) can be set by one pulse of appropriate amplitude or by an appropriate number of consecutive pulses of a fixed voltage. The study also presents a model for the dynamics of resistance switching, which accounts for the observed "wavy" behavior. The model divides the pad area into zones with different propagation and nucleation kinetics, each ruled by the Kolmogorov-Avrami-Ishibashi (KAI) model. The model provides an analytical expression for the memristive response based on the physical description of the ferroelectric domain dynamics. The results demonstrate that the resistance can be continuously and reversibly tuned over more than two orders of magnitude by varying the pulse amplitude and/or the pulse number. These features qualify FTJs as memristive devices, which is an improvement over previous memristors with a purely electronic mechanism where the resistance contrast is no better than a factor of two. The study also shows that the resistance switching behavior can be modeled using a simple model of domain nucleation and growth in a heterogeneous medium. The results highlight the advantage of using well-established physical phenomena like ferroelectricity in the design of novel memristive systems. The findings open new perspectives for ferroelectrics in next-generation neuromorphic computational architectures.