A neural circuit algorithm for action grammar
- 1Florida Atlantic University, New York University
Abstract
How does the brain generate sequences of discrete elements, such as actions or words, that follow an abstract rule (a grammar)? Motivated by an experiment in which monkeys learned to draw multi-object images according to grammatical rules, we trained a recurrent neural network to generate sequences Am Bn: producing shape A m times, then B n times, or vice versa. Our findings are threefold. First, the network plans the sequence by moving to an initial state encoding m and n along orthogonal directions for upcoming execution. Second, the network repeats each shape by progressing along a shape-specific manifold. Third, transitions from Am to Bn follow parallel trajectories between manifolds across values of n, thus preserving n. Each of these is an experimentally testable prediction. The network generalizes to longer novel sequences by starting further along the same orthogonal directions. These results reveal a neural circuit implementation of a symbolic algorithm for generating rule-based action sequences.
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BibTeX
@misc{liu2026neural,
title = {{A neural circuit algorithm for action grammar}},
author = {Y. Liu and L. Y. Tian and J. B. Tenenbaum and W. A. Freiwald and X.-J. Wang},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.757194},
url = {https://www.biorxiv.org/content/10.64898/2026.10.06.757194v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A neural circuit algorithm for action grammar
AU - Y. Liu
AU - L. Y. Tian
AU - J. B. Tenenbaum
AU - W. A. Freiwald
AU - X.-J. Wang
PY - 2026
DA - 2026/10/06
PB - bioRxiv
DO - 10.64898/2026.10.06.757194
UR - https://www.biorxiv.org/content/10.64898/2026.10.06.757194v1
AB - How does the brain generate sequences of discrete elements, such as actions or words, that follow an abstract rule (a grammar)? Motivated by an experiment in which monkeys learned to draw multi-object images according to grammatical rules, we trained a recurrent neural network to generate sequences Am Bn: producing shape A m times, then B n times, or vice versa. Our findings are threefold. First, the network plans the sequence by moving to an initial state encoding m and n along orthogonal directions for upcoming execution. Second, the network repeats each shape by progressing along a shape-specific manifold. Third, transitions from Am to Bn follow parallel trajectories between manifolds across values of n, thus preserving n. Each of these is an experimentally testable prediction. The network generalizes to longer novel sequences by starting further along the same orthogonal directions. These results reveal a neural circuit implementation of a symbolic algorithm for generating rule-based action sequences.
ER -