Attractor networks can realistically describe neurophysiological processes while providing useful computational modules for pattern recognition, signal restoration, and feature extraction. To implement attractor networks in small-area integrated circuits, the development of a hybrid technology including CMOS transistors and resistive switching memory (RRAM) is essential. This work presents a summary of recent results toward implementing RRAM-based attractor networks. Based on realistic models of HfO 2 RRAM devices, we design and simulate recurrent networks showing the capability to train, recall and sustain attractors. The results support the feasibility of RRAM-based bio-realistic attractor networks.