From pulses to plasticity: Analytical tools for memristive synapse design
[EN] Neuromorphic device design demands a clear understanding of the dynamics governing conductance modulation under external stimuli. Many synaptic memristors can be described by a quasi-linear model, where a memory variable relaxes between two limiting states. Here, we derive analytical expression...
| Authors: | , |
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| Format: | article |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/230608 |
| Online Access: | https://riunet.upv.es/handle/10251/230608 |
| Access Level: | Open access |
| Keyword: | Memristor devices Neuromorphic engineering Artificial neural networks Perovskites Nanostructures Neuroscience |
| Summary: | [EN] Neuromorphic device design demands a clear understanding of the dynamics governing conductance modulation under external stimuli. Many synaptic memristors can be described by a quasi-linear model, where a memory variable relaxes between two limiting states. Here, we derive analytical expressions for the response of such systems to trains of voltage pulses, providing closed formulations for paired-pulse facilitation (PPF), convergent potentiation, and frequency-dependent gain. This approach predicts how the memory variable evolves toward stationary values determined by device and stimulation parameters, offering a compact alternative to numerical simulations. We experimentally validate the model using a nanofluidic memristor based on a nanoporous membrane, showing that the predicted convergence closely matches measured potentiation and that the analytical PPF trends reproduce experimental data. These results establish a unified framework for describing spike-driven plasticity and enable reliable cross-comparison of synaptic behavior across memristive systems, facilitating their integration into neuromorphic circuits. |
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