Gradient descent was invented before Minsky. Imo, Minsky produced some vague writings, with no significant practical impact, but this is enough for some people to claim his founder's role in the field.
Minsky was actually a pioneer in the field, when it came to working with real networks. Compare
[0] “A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement”, Harvard University Psychological Laboratories, Cambridge, MA, January 8, 1952
[1] “Neural Nets and the Brain Model Problem”, Princeton Ph.D dissertation, 1954
In comparison, Frank Rosenblatt's Perceptron at Cornell was only built in 1958. Notably, Minsky's SNARC (1951) was the first learning neural network.
> when it came to working with real networks. Compare
my understanding is that that no one knows what that SNARK thing was, he built something on the grant, abandoned it shortly after that, and only many years later he and fanboys started using it as foundation of bold claims about his role in the field.
> “Multiple simultaneous optimizers” search for a (local) maximum value of some function E(λ1, …, λn) of several parameters. Each unit Ui independently “jitters” its parameter λ1, perhaps randomly, by adding a variation δi(t) to a current mean value μi. The changes in the quantities λi and E are correlated, and the result is used to slowly change μi. The filters are to remove DC components. This technique, a form of coherent detection, usually has an advantage over methods dealing separately and sequentially with each parameter.
[0] “A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement”, Harvard University Psychological Laboratories, Cambridge, MA, January 8, 1952
[1] “Neural Nets and the Brain Model Problem”, Princeton Ph.D dissertation, 1954
In comparison, Frank Rosenblatt's Perceptron at Cornell was only built in 1958. Notably, Minsky's SNARC (1951) was the first learning neural network.