Work

Spiking Neural Network with Silicon Oxide Memristive Devices in the Subthreshold Regime

Year: 2025

Type: conference-paper

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.

Source: IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI)

DOI: 10.1109/seai65851.2025.11108769

Authors: Viet Cuong Vu, Daniel J. Mannion, Dovydas Joksas, Wing H. Ng, Adnan Mehonic, Anthony Kenyon

Institution: University College London

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Award: AI applications of neuromorphic memristors