Has a deep-network critical-period model ever predicted or matched the timing of a human critical period (e.g. amblyopia) — closing the biology→DL→clinic loop?

Achille, Rovere & Soatto (2019) validate deep-network critical periods against animal models — the onset/length dependence matches monocular-deprivation results in kittens (claim-deep-nets-have-critical-learning-periods-timed-like-animals, claim-monocular-deprivation-permanently-rewires-visual-cortex). The open loop-closure candidate runs the arrow the other way: has anyone used the deep-net model to predict or quantitatively match the timing of a human critical period — the amblyopia treatment window, say — turning a biology→DL analogy into a DL→clinic prediction?

Why it matters

This is the difference between a pretty analogy and a working model. If a purely computational critical-period model reproduced the human window's onset and duration, it would be strong evidence that the biological critical period really is (at least partly) an information-dynamics phenomenon rather than a pruning-chemistry one — the deflationary claim in claim-critical-periods-arise-from-information-plasticity-not-biology. It is also the specific gap the capture flagged: the human-critical-period ↔ deep-net link "deserves one more primary source before publishing." The animal link is solid; the human link is asserted by analogy, not demonstrated.

What I'd need to answer it

• Search for follow-on work (Achille/Soatto and others post-2019) that fits or predicts human critical-period timing from a network model.
• A primary clinical source on the amblyopia/cataract treatment window's actual onset and duration, to check any claimed quantitative match.
• Distinguish "the model shows a critical period" (established) from "the model predicts the human window's timing" (the real loop closure).

Candidate next moves

• If such work exists, it upgrades both critical-period notes off seedling and may warrant its own claim-note on the biology→DL→clinic loop.
• If it does not, keep the human extension explicitly marked as analogy in the two claim-notes, and treat this as a live research thread rather than a settled bridge.

Progress

2026-07-18 (partial — stays open). A batch capture (10-inbox/raw/2026-07-14-has-a-deep-network-critical-period-model-ever.md) searched for a positive instance and found none, promoted here as claim-no-dnn-model-has-matched-human-critical-period-timing. The founding paper's own scope is now nailed down: claim-founding-dnn-critical-period-paper-validated-on-animal-data-only (animal data only, human curve not regressed for lack of data) and claim-achille-soatto-disclaim-dnn-as-valid-model-of-biology (authors decline the biology-model reading outright). Two follow-on papers (Fukase et al. 2025, arXiv:2506.15954; Cai et al. 2025, arXiv:2511.14440) do not perform a quantitative human-timing match either.

This answers the "why hasn't it been done" half — a data-availability gap the founding authors named in 2019 — but does not close the existence question, which is search-scoped and cannot prove no such work exists. Left open. The two specific unclosed next hops, both flagged by the capture and neither read this run: (1) the Project Prakash / Sinha-lab paper (Vogelsang et al. 2024, Science), the closest known instance of a DNN run against real human vision-restoration data — read it for whether any critical-period timing parameter is fit, not just color-cue reliance; (2) von Noorden (1981) and Taylor et al. (1979), the primary human amblyopia-timing sources Achille et al. themselves cite, which would supply the clinical window a DNN curve could be regressed against. When one of these produces a genuine timing match — or a confident exhaustion of the search — this closes.