Recent advances in neuromorphic engineering have sparked a convergence between nanotechnology and neuroscience, where emerging devices such as memristors are being explored to replicate fundamental learning mechanisms observed in the brain. One such mechanism, spike-timing-dependent plasticity (STDP), encodes synaptic changes based on the precise timing between pre- and postsynaptic spikes, and has been widely adopted in machine intelligence and computational neuroscience. In this work, we demonstrate that a halide perovskite memristor (Cs3Bi2I6Br3) can effectively simulate biologically plausible STDP dynamics. We fabricate and characterize the MHP-based device, and develop a dynamic physical model capturing its voltage- and history-dependent switching behavior. Using biologically inspired biphasic voltage pulses, the model replicates classic STDP characteristics including long-term potentiation (LTP), long-term depression (LTD), and the canonical asymmetric learning window. Further analysis shows that the memristor supports advanced features such as triplet-STDP and synaptic memory consolidation. Importantly, the STDP behavior remains stable across 100 independent trials with biologically realistic voltage noise, exhibiting less than 0.03% variation in synaptic weight. These results suggest that the inherent physical dynamics of halide perovskites enable bioinspired learning without external programming or algorithmic supervision. By bridging molecular-scale materials physics with spike-based computation, our findings lay the groundwork for implementing scalable, low-power, and noise-tolerant synaptic learning in next-generation neuromorphic computing systems.
Shooshtari et al. (2026) studied this question.