The field of spiking neural networks (SNNs) is rapidly advancing, with a focus on developing efficient and accurate models for cognitive state monitoring. Recent research has explored the use of SNNs for classification tasks, such as cognitive load classification in air traffic control and mental workload classification. These models have shown promising results, with some achieving competitive performance with traditional machine learning models. The use of neuromorphic hardware and event-driven processing has also been investigated, offering a potential solution for low-power and fast processing. Furthermore, SNNs have been applied to various domains, including brain activity biomarker detection in multiple sclerosis patients and embedded deep learning for bio-hybrid plant sensors. Noteworthy papers in this area include: Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control, which demonstrates the feasibility of event-driven neuromorphic systems for ultra-low-power cognitive state monitoring. SpikeMatch: Semi-Supervised Learning with Temporal Dynamics of Spiking Neural Networks, which introduces a novel SSL framework for SNNs that leverages temporal dynamics for diverse pseudo-labeling.
Spiking Neural Networks for Efficient Cognitive State Monitoring
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Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Machine Learning and AI Applied to fNIRS Data Reveals Novel Brain Activity Biomarkers in Stable Subclinical Multiple Sclerosis
SpikeMatch: Semi-Supervised Learning with Temporal Dynamics of Spiking Neural Networks
Accuracy-Robustness Trade Off via Spiking Neural Network Gradient Sparsity Trail
S$^2$NN: Sub-bit Spiking Neural Networks
Hybrid Layer-Wise ANN-SNN With Surrogate Spike Encoding-Decoding Structure
PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
DelRec: learning delays in recurrent spiking neural networks
Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels
Random Feature Spiking Neural Networks
Low Rank Gradients and Where to Find Them