Addressing the lack of neuromorphic data in low SNR scenarios

Tools

Perry, Ross and Di Caterina, Gaetano and Bihl, Trevor J. and Combs, Kara (2025) Addressing the lack of neuromorphic data in low SNR scenarios. In: Hawaii International Conference on System Sciences, 2026-01-06 - 2026-01-09. (In Press)

[thumbnail of Perry-etal-HICSS-2026-Addressing-the-lack-of-neuromorphic-data-in-low-SNR-scenarios] Text. Filename: Perry-etal-HICSS-2026-Addressing-the-lack-of-neuromorphic-data-in-low-SNR-scenarios.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 1 January 2099.
Download (8MB) | Request a copy

Abstract

Neuromorphic systems have shown promising benefits to edge computing systems. However, the scarcity of event-based data is limiting progress in the development of models optimised for asynchronous event-based inputs. Event-based systems have shown promising solutions for addressing low signal-to-noise ratio (SNR) conditions, particularly in scenarios where traditional frame-based computer vision approaches break down. We present a recording methodology that captures low-SNR motion using two spatially similar species of blowfly as a proxy for small, erratic objects in a high dynamic range environment. Using this dataset, we evaluate a traditional frame-based convolutional neural network, ResNet50, and show that its rich spatial capabilities fails to distinguish between our two classes by achieving a test accuracy of 51\%. These results highlight the need for event data and for event-native models such as Spiking Neural Networks (SNNs) that can utilise spatial-temporal features for classification tasks.

ORCID iDs

Perry, Ross ORCID logoORCID: https://orcid.org/0009-0008-5315-2987, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897, Bihl, Trevor J. and Combs, Kara;
CORE (COnnecting REpositories)