Backward Propagation

Backward propagation (BP), a cornerstone of training artificial neural networks, aims to efficiently compute gradients for updating model parameters. Current research focuses on improving BP's efficiency and applicability across diverse architectures, including transformers, spiking neural networks, and optical neural networks, exploring alternatives like forward-only methods and modifications to reduce computational cost and memory requirements. These advancements are crucial for training increasingly complex models, enabling progress in areas like large language models, neuromorphic computing, and efficient solutions to partial differential equations.