Vu, VC;
Mannion, DJ;
Joksas, D;
Ng, WH;
Mehonic, A;
Kenyon, A;
(2025)
Spiking Neural Network with Silicon Oxide Memristive Devices in the Subthreshold Regime.
In:
Proceedings of the 5th International Conference on Software Engineering and Artificial Intelligence (SEAI) IEEE 2025.
(pp. pp. 340-344).
IEEE
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Abstract
This study explores silicon oxide-based memristive devices in spiking neural networks (SNNs), leveraging the subthreshold regime for performance and robustness. A bio-inspired homeostasis-driven dropout mechanism dynamically adjusts synaptic conductance to counteract memristor non-idealities like variability and noise. This adaptive regularization stabilizes networks and reduces overfitting. Experimental results show that convolutional SNNs using this approach achieve superior accuracy, outperforming models with faulty devices. These findings highlight the potential of subthreshold memristive SNNs for scalable, low-power neuromorphic computing, with future applications in bio-signal processing and anomaly detection.
| Type: | Proceedings paper |
|---|---|
| Title: | Spiking Neural Network with Silicon Oxide Memristive Devices in the Subthreshold Regime |
| Event: | 5th International Conference on Software Engineering and Artificial Intelligence (SEAI) IEEE 2025 |
| Dates: | 20th-22nd June 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SEAI65851.2025.11108769 |
| Publisher version: | https://doi.org/10.1109/seai65851.2025.11108769 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216049 |
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