Fast social-like learning of complex behaviors based on motor motifs

Carlos Calvo Tapia, Ivan Y. Tyukin, Valeri A. Makarov

correctmedium confidence

Category: math.DS
Journal tier: Specialist/Solid
Processed: Sep 28, 2025, 12:56 AM
arXiv Links: Abstract ↗PDF ↗

Audit review

The paper proves exponential learning of time intervals when Wy=Wx (Theorem 1), provides the closed-form γ dynamics and a regression-based detector to identify correct edges (Theorem 2), and shows a rewiring algorithm converging in at most n−1 periods (Theorem 3). The candidate solution reproduces the same core identities and results; for Task A it follows the paper’s Appendix A essentially verbatim, while for Task B it supplies an alternative but valid group-theoretic proof (via the relative permutation A[k]) of the n−1 bound. Minor gaps in both texts concern small justifications (e.g., uniform positivity of p, or constancy arguments for qj/pj), but these are fixable and do not affect the main claims.

Referee report (LaTeX)

\textbf{Recommendation:} minor revisions

\textbf{Journal Tier:} specialist/solid

\textbf{Justification:}

The paper presents a clear adaptive learning rule for GLV/WLC networks and proves three main results: exponential learning of motif durations, a regression-based detector for edge correctness, and a linear-time rewiring algorithm. The mathematical derivations are largely correct and consistent, with concise appendices. Some steps (e.g., uniform positivity bounds and aspects of the detector’s necessity proof) would benefit from fuller explanations, but these are minor and do not undermine the main claims. The experimental validation on mobile robots adds credibility and relevance.