Fukushima's Neocognitron (1979/1980) introduced the core CNN architecture — weight-shared local features plus downsampling — but was not trained by backpropagation

The Neocognitron introduced what became the convolutional neural network's defining structure: layers of weight-shared local feature detectors alternating with downsampling ("S-cells" and "C-cells"), giving shift-invariant pattern recognition. Fukushima's own 1980 abstract frames it in terms of unsupervised self-organization — not "convolution," not "first," and with no priority claim. The "first CNN" credit is historians' (chiefly Schmidhuber, who calls it "perhaps the first artificial NN that deserved the attribute deep"), with the sharp qualifier the retellings often drop: it was not trained by backpropagation — its learning was unsupervised self-organization, and the backprop-trained CNN is LeCun 1989 (claim-lecun-1989-first-practical-recognition).

So the honest form: Neocognitron is the architectural ancestor of the CNN, LeCun 1989 is where that architecture met backpropagation. Secondary sources disagree on how strongly to state the priority — even two Wikipedia articles use different strengths — which is the same "who invented it" compression the myth ledger tracks. The Schmidhuber single-witness caveat applies (entity-juergen-schmidhuber). See moc-backpropagation-origins.