Learning Rule

Learning rules govern how artificial neural networks and other machine learning models adapt and learn from data, aiming to optimize performance on a given task. Current research focuses on understanding the mathematical properties of various learning rules, including their relationship to gradient descent, their vulnerability to adversarial attacks (especially in spiking neural networks), and their impact on representation learning and network dynamics across different architectures. This research is crucial for improving the efficiency, robustness, and interpretability of machine learning models, with implications for diverse applications ranging from robotics and anomaly detection to knowledge graph completion and brain-computer interfaces.