A COMPUTATIONAL MODEL OF LEARNING AND MEMORY USING STRUCTURALLY DYNAMIC CELLULAR AUTOMATA

The paper defines the update rule ϕ(x) = 1/2·x(D^{-1}C + I), represents rewards by pre‑wired, high‑conductance edges to a specialized reward node, and uses a greedy move to the maximum in the closed neighborhood (including the self‑node), backed only by simulations—not a theorem about shortest‑path or distance‑bounded convergence. The candidate solution refutes two stronger claims (about reaching the target in dist(s,t) steps) that the paper does not actually state, and also adopts a different reward encoding (as a self‑loop at t). While the candidate correctly highlights a genuine pitfall of the r-step greedy rule under the lazy operator (e.g., from a non‑leaf neighbor of t with r = 1 the agent stays put), this does not contradict any formal result in the paper; rather, it exposes missing theoretical guarantees and unclarified modeling choices in the paper. Hence both are incomplete: the paper lacks formal guarantees/assumptions, and the model audits claims that are not present in the paper and relies on a reward encoding that diverges from the paper’s specification.