Dead Neuron

"Dead neurons," or inactive neurons in artificial neural networks, represent a significant challenge in deep learning, impacting model performance and efficiency. Current research focuses on understanding the causes and consequences of this phenomenon across various architectures, including spiking neural networks (SNNs) and deep reinforcement learning models, and developing methods to mitigate its effects, such as adaptive threshold learning in SNNs and pruning techniques that selectively reactivate dormant neurons. Addressing dead neurons is crucial for improving model accuracy, robustness, and resource efficiency, with implications for applications ranging from image recognition to brain-computer interfaces.