I mean if we want to get pedantic I'm pretty sure Shannon used "backpropagation" for machine learning before either was called such.
Feedback for the purpose of regulating the state of a machine in response to input dates to antiquity, if we're really getting absurd. The formal definition is also debatable, I think Maxwell has the strongest claim.
> In the 1960s, academics including... arrived at the theory of backpropagation.
It was clearly phrased this way specifically because backprop is just the chain rule applied in a particular direction, and as such has been invented and reinvented over and over by every one under the sun. Hell, a lazy googling says gradient descent goes back to Cauchy.
I'd say Gottfried W. Leibniz is the true author, as it's all comes down to calculus. The particular implementation for "neural nets" is just a special case of function minimization by taking derivatives.
I like zoom-out views. To push what you describe further, it is essentially what ancient humans or their non-hominid forebears did subconsciously when calculating optimum motion trajectories to catch or spear prey while hunting... merely a version in formal notation ... we can thank the zero of India (https://en.wikipedia.org/wiki/0#History), the Persians (https://en.wikipedia.org/wiki/Algorithm#Etymology), the Islamic renaissance in Europe (https://mitpress.mit.edu/books/islamic-science-and-making-eu...) and numerous others for the slow development of the requisite formal maths. But a rose by any other name would smell as sweet. And perhaps, in the context of the stupefyingly deferred emergence of zero, even nameless!
> To push what you describe further, it is essentially what ancient humans or their non-hominid forebears did subconsciously when calculating optimum motion trajectories to catch or spear prey while hunting
