Oscillating Behaviors - There is a constant push for ever increasing performance in traditional computing systems, leading to high power consumption and, in the end, to the incapacity of conventional electronics to handle heavy computing tasks, which usually require learning features. Thus, the development of novel nanoelectronic devices with inherent neuromorphic characteristics and a low energy footprint has become a viable alternative. In order to simulate neuromorphic features utilizing memristive devices, the threshold switching effect is critical, which can be seen in the rich dynamics of metallic conductive filament (CF). In this paper, a realistic model of the unipolar nature of CBRAM devices is exploited to create a memristor-based oscillator that can integrate neuromorphic features. Bipolar memristive devices have been used to match the weight of the neurons in a crossbar configuration. The used physical model for these memristors was fitted to fabricated devices in order to achieve the expected accuracy in the circuit simulation. The oscillator’s output signal and behavior matched the theoretical background of biological neurons. Thus, this approach can be considered as the first step towards the development of low-power oscillation-based neuromorphic hardware with biological-like behavior.