The "Ivakhnenko 1965 = first deep learning" characterization is Schmidhuber's, hedged in his own peer-reviewed text, and rests on GMDH's layer-wise regression — not gradient training

The claim "deep learning was invented in the Soviet Union in 1965" circulates widely. Its evidentiary structure, established at capture level:

• The characterization's source is Schmidhuber, and his peer-reviewed wording carries a hedge his popular restatements (and Wikipedia's echoes) drop: GMDH networks "were perhaps the first DL systems of the Feedforward Multilayer Perceptron type." The same drop-the-hedge pattern appears in the Amari thread (claim-wikipedia-amari-sgd-citogenesis).
• The mechanism is not gradient descent. GMDH grows and trains layers incrementally by regression analysis (Schmidhuber's own 2014 Connectionists wording: layers "incrementally grown and trained by regression analysis") — so even if "deep," it is a different training family from backpropagation/SGD; a priority claim for depth, not for the algorithm.
• The quantitative anchor is unverified. "A 1971 paper described a deep network with the equivalent of eight layers" exists here only at Tier 3 (Wikipedia). [unverified-quant — needs primary: Ivakhnenko 1971, "Polynomial theory of complex systems," IEEE Trans. SMC.]
• The omission half is settled separately: RHW 1986 cites neither Ivakhnenko nor Amari (claim-rhw-1986-reference-list-four-works, now carrying Hinton's own "previous inventors that we failed to cite" admission).

Treat as: uncontested that GMDH 1965 exists and layer-wise-builds multilayer models; Schmidhuber-shaped in the "first deep learning" framing until an Ivakhnenko primary is read. Same source-critical posture as myth-amari-first-sgd-mlp. Cluster: moc-backpropagation-origins.