Forward Forward Learning

Forward-forward learning (FFL) is a biologically-inspired alternative to backpropagation for training neural networks, aiming to achieve efficient, local learning without the need for error signals propagated backward through the network. Current research focuses on improving FFL's performance and generalization capabilities through modifications like distance metric learning, neural polarization, and incorporating cortical loop-like feedback mechanisms, often applied to convolutional and spiking neural networks. These advancements demonstrate FFL's potential for energy-efficient on-chip learning and robust performance in various tasks, including image classification and out-of-distribution detection, while also offering insights into biological learning processes.