A bio-inspired hybrid lifelong learning based on self-selective memristors

<title>Abstract</title> <p> Lifelong learning, the ability to acquire and retain knowledge continually, is essential for artificial intelligence operating in dynamic, open-ended environments, yet remains limited by catastrophic forgetting, limited generalization, and high energy costs. These challenges stem from a dual disconnect: between algorithms and hardware, and from the oversimplified adoption of singular biological mechanisms. We present a co-design paradigm that bridges this gap by integrating complementary neurobiological principles into a hardware-native learning system. Our approach unites a hybrid lifelong learning algorithm—fusing <italic>Drosophila</italic> -inspired dynamic active forgetting with human-derived metaplasticity—with a self-selective memristor (SSM) that intrinsically emulates these synaptic dynamics. The algorithm balances synaptic pruning for adaptability with consolidation for stability, while the SSM hardware physically instantiates this stability-plasticity continuum through its tunable conductance decay and self-rectifying properties, eliminating cross-talk without external selectors. With a 10% read margin, the passive crossbar array integrated with the proposed device achieves terabit-scale storage capacity. Evaluated on 20 sequential CIFAR-variant tasks using hardware-calibrated simulation with per-tile hardware validation, our system projects 3.42× speedup and 91× energy improvement over CMOS accelerators. This work establishes a scalable, hardware-algorithm-integrated foundation for the efficient deployment of lifelong learning in real-world systems. </p>