Recurrent neural network (RNN)

A neural network whose hidden state follows a recurrence $h_t=F(h_{t-1},x_t)$ with shared parameters. It represents dependence across sequence positions through its hidden state. The standard recurrence is sequential across positions, and differentiation through many steps multiplies step Jacobian matrices, potentially causing a vanishing gradient or an exploding gradient.

A recurrent architecture that uses learned gates to regulate state preservation, replacement or readout. Gating can improve useful memory and learning over long intervals, but does not by itself establish biological implementation or solve arbitrary temporal credit assignment.

A recurrent network with a continuous family of stable or approximately stable activity patterns, such as a localized bump at any angle on a ring. Recurrent dynamics maintain the represented variable while motion-related input updates it and sensory input anchors it. Postulating the attractor family does not by itself explain its developmental origin.