Spike Threshold

Spike threshold, a crucial parameter in spiking neural networks (SNNs), determines when a neuron fires, significantly impacting network performance and learning. Current research focuses on moving beyond treating the threshold as a fixed hyperparameter, instead exploring adaptive threshold learning mechanisms that dynamically adjust it based on input data and network activity, improving training efficiency and accuracy. This research is driven by the need to address issues like "dead neurons" (neurons that never fire) and enhance SNN robustness against noise and adversarial attacks, ultimately aiming to improve the efficiency and reliability of SNNs for applications like biosignal processing and object detection.