Addressing the Lack of Neuromorphic Data in Low SNR Scenarios

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.