Error Backpropagation

Error backpropagation (backprop) is a fundamental algorithm for training artificial neural networks, aiming to efficiently adjust network weights to minimize prediction errors. Current research focuses on addressing backprop's limitations, such as susceptibility to local minima, vanishing/exploding gradients, and scalability issues, through alternative training methods like those inspired by multi-agent systems or employing modified learning rules that incorporate environmental cues or probabilistic latent representations. These advancements aim to improve training efficiency, robustness, and biological plausibility, ultimately impacting the performance and applicability of neural networks across diverse fields.