A neural circuit algorithm for action grammar
Motivated by monkey experiments, this study demonstrates that a recurrent neural network generates rule-based action sequences by planning initial states along orthogonal directions, executing shapes on specific manifolds, and preserving sequence parameters during transitions, thereby revealing a neural circuit implementation of a symbolic grammar algorithm.