Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic Functionality
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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/417586 |
| Acceso en línea: | http://hdl.handle.net/10261/417586 |
| Access Level: | acceso embargado |
| Palabra clave: | Halide perovskite memristor Neuromorphic computing Noise robustness Spike-timing-dependent plasticity (STDP) Synaptic plasticity Triplet-STDP |
| Sumario: | 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. |
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