Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control

• URL: http://arxiv.org/abs/2509.21345v2
• Date: Fri, 03 Oct 2025 14:35:40 GMT
• Title: Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
• Authors: Jiahui An, Chonghao Cai, Olympia Gallou, Sara Irina Fabrikant, Giacomo Indiveri, Elisa Donati,
• Abstract summary: This paper presents a neuromorphic system for cognitive load classification in a real-world setting, an Air Traffic Control (ATC) task, using a hardware implementation of Spiking Neural Networks (SNNs)<n>EEG and eye-tracking features, extracted from an open-source dataset, were used to train and evaluate both conventional machine learning models and SNNs.<n>To enable deployment on neuromorphic hardware, the model was quantized and implemented on the mixed-signal DYNAP-SE chip.
• Score: 1.2545957939431476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a neuromorphic system for cognitive load classification in a real-world setting, an Air Traffic Control (ATC) task, using a hardware implementation of Spiking Neural Networks (SNNs). Electroencephalogram (EEG) and eye-tracking features, extracted from an open-source dataset, were used to train and evaluate both conventional machine learning models and SNNs. Among the SNN architectures explored, a minimalistic, single-layer model trained with a biologically inspired delta-rule learning algorithm achieved competitive performance (80.6%). To enable deployment on neuromorphic hardware, the model was quantized and implemented on the mixed-signal DYNAP-SE chip. Despite hardware constraints and analog variability, the chip-deployed SNN maintained a classification accuracy of up to 73.5% using spike-based input. These results demonstrate the feasibility of event-driven neuromorphic systems for ultra-low-power, embedded cognitive state monitoring in dynamic real-world scenarios.

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