Feedforward

Feedforward is a term used to describe systems in which the flow of information moves in one direction from input toward output, without cycles that feed the output back into the system. The concept appears in several disciplines, including engineering, machine learning, and neuroscience, and is often contrasted with feedback-based architectures.

In control theory, feedforward control uses a model of the system and a prediction of disturbances to apply corrective action before they affect the output. This can improve response speed and reduce steady-state error for disturbances that are measurable or repeatable. Feedforward control is typically used together with feedback control: the feedforward path handles predictable effects, while the feedback path corrects residual errors due to modeling errors or unmeasured disturbances. The effectiveness of feedforward control depends on the accuracy of the process model and the availability of disturbance information.

In machine learning and artificial neural networks, a feedforward network is an arrangement in which information moves only forward from input to hidden layers to output, with no cycles. This contrasts with recurrent networks that reuse outputs as inputs. Feedforward networks include multilayer perceptrons and many convolutional networks, and training is typically done by backpropagation to minimize error on labeled data. They are used for classification, regression, and function approximation.

In neuroscience, feedforward processing describes the sequential transmission of sensory information from lower to higher brain areas, such as from the retina to the visual cortex. This fast, one-way flow is often complemented by feedback connections from higher areas that modulate activity in earlier stages. Feedforward processing supports rapid perception and initial feature extraction, while recurrent and feedback mechanisms contribute to refinement, context, and learning.

Overall, feedforward architectures emphasize unidirectional signal flow and predictive action, but they differ in application across disciplines. They are generally robust to simple, repeatable disturbances but require accurate models or priors and may be less adaptable to unexpected changes compared with architectures that incorporate feedback.