Feedback Alignment

Feedback alignment (FA) is a biologically-inspired alternative to backpropagation for training artificial neural networks, aiming to overcome the latter's limitations in biological plausibility and scalability. Current research focuses on adapting FA to various architectures, including spiking neural networks, graph neural networks, and transformers, and on improving its performance and theoretical understanding through techniques like augmented feedback and product feedback alignment. This approach holds significant promise for energy-efficient neuromorphic computing and for developing more biologically realistic learning models, potentially impacting both the theoretical foundations of deep learning and its practical applications in resource-constrained environments.