Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Abstract

We develop a model of Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti memristive devices and demonstrate, both experimentally and numerically, an inverted spike-rate-dependent plasticity effect. The effect consists of the reduction of the learning rate with an increase in the frequency of spikes generated by the phase-locked loop neuron. The memristor model uses two internal state variables representing the number of complete filaments and the concentration of the charged traps. While the former state variable defines the device resistance and is associated with the distribution of oxygen vacancies, the latter affects the internal electric field and modulates the migration of vacancies. Several neural circuit configurations that include pairs and populations of memristively coupled neurons are analyzed numerically. The results of this study may contribute to the development of large-scale self-organized artificial cognitive systems based on neural synchrony.