Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic Functionality
Instituto de Microelectrónica de Sevilla · Consejo Superior de Investigaciones Científicas · +2 more institutions
Abstract
) 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…
Citation impact
- FWCI
- 61.72
- Percentile
- 100%
- References
- 50
Authors
6- MSMostafa ShooshtariCorresponding
Instituto de Microelectrónica de Sevilla
- SKSo-Yeon Kim
Consejo Superior de Investigaciones Científicas, Instituto de Tecnología Química, Universitat Politècnica de València
- SPSaeideh Pahlavan
Instituto de Microelectrónica de Sevilla
- TSTeresa Serrano-Gotarredona
Instituto de Microelectrónica de Sevilla
- JBJuan Bisquert
Consejo Superior de Investigaciones Científicas, Instituto de Tecnología Química, Universitat Politècnica de València
Topics & keywords
- Neuromorphic engineering
- Memristor
- Synaptic plasticity
- Long-term potentiation
- Bridging (networking)
- Metaplasticity
- Perovskite (structure)
- Photonics