The C in CNN isn't "Convolution" for no reason. It came from work with convolutional filters (yay Sobel kernels!) which at it's height became filter banks and gabor filters and so on before neural networks pretty much killed off handcrafted feature development. Every explanation of how CNNs work still falls back to the original convolutional kernel intuition.
The first N in CNN is "Neural" for a reason.
Decision trees are called 'trees' for, more or less, the same reason.
ie., the diagrammed shape of a decision tree looks a little like the branches of a real one.
likewise, in the 50s where diagramming the earliest networks they were aiming to immitate a similar real-world structure.
Better that they had called them 'Variable Activation Networks' or some such, and none of this superstition would have started
But that's the thing: they didn't. Instead, they called them "neural networks". It wasn't random.
It feels like part of the field now wants to pretend it was never about how to make a machine think. "No, we're only doing abstract maths, only going on self-contained explorations of CS theory." Yeah, right. That feels like a reaction to the new wave of AI hype in business. Now that the rubes are talking about thinking machines again, better distance themselves from them, lest we be confused for those loonies.
Thing is, the field was always driven in big part by trying to catch up with nature. It took inspiration from neuroscience, much like neuroscience borrowed some language from CS, both for legitimate reasons. A brain is a computer. It's precisely where the CS and neuroscience have an overlap - they're studying the same thing, just from opposite directions. It's just silly to play the "oh my field is better and your field doesn't know shit" game.
> Decision trees are called 'trees' for, more or less, the same reason.
Decision trees are called after the data structure, which is a way to express a mathematical object, which is older than CS and got that name from... who knows, but my money is on "genealogical tree", which itself is called a "tree" because people back then liked to tie everything to trees (symbol of growth) and flowers and cute animals (symbols of making babies).
The field inherited "trees" from the past. "Networks", too. But "neural" - that was a modern analogy the field itself is responsible for.
There's absolutely no mention of biological inspiration whatsoever. At the same time, one can point to a long and rich history of convolutional filters being used in signal processing. And then there's the name, Convolutional Neural Network. The entire concept of a CNN is framed as a series of learned filters.
Regardless, Le Cun is not the first to describe CNNs, merely one of the first to use them for OCR (specifically for hand-written text).
The first neural network arch to use convolutions instead of matmuls was this[2], from the year of our lord 1988. This in turn is based on Fukushima's "neocognitron"[3] (1980), which is based on the visual cortex of felines (from work done by Hubel and Wiesel in the 50s/60s).
I guess it is not super surprising you might be confused – Le Cun seems a bit more reticent than average to cite the work he's building on top of, and when he does it is frequently in reference to his own prior work. So if that is where you're getting your picture of artificial neural network history, your skewed perception makes sense.
[1] https://ieeexplore.ieee.org/abstract/document/41400
[2] https://proceedings.neurips.cc/paper/1987/file/98f1370821019...
[3] https://www.cs.princeton.edu/courses/archive/spr08/cos598B/R...
