Backpropagationia
Backpropagationia is a term used in some circles to denote a family of ideas that seek to augment standard backpropagation with additional feedback mechanisms during neural network training. The term is not part of a formal, widely accepted algorithm; rather it aggregates several proposals that extend backpropagation by incorporating auxiliary signals, neuromodulatory-style gating, and meta-learning components to improve learning dynamics.
Conceptually, backpropagationia may involve adding auxiliary loss terms attached to intermediate representations, using learned controllers to
Relation to standard backpropagation: It does not replace the basic calculation of gradients but enriches the
Current status and usage: The term is more common in theoretical discussions, speculative work, or online discourse
See also: Backpropagation, gradient descent, multi-task learning, neuromodulation, meta-learning.