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.
That is definitely not the first paper describing a CNN. That is not even the first paper by Le Cun describing CNNs (he was already on them as early as 1989[1]).
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.
Thanks, I was looking for something to do with early work and saccades, didn't find that, but found this;
"The most influential of these early discussions was probably the 1943 paper of Warren McCulloch and Walter Pitts in which activity in neuronal* networks was identified with the operations of the propositional calculus. Actual simulations of recognition automata based on networks were carried out by Frank Rosenblatt
before 1958 but the theoretical limitations of his "perceptrons" were soon pointed out by Marvin Minsky and Seymour Papert"
I don't know why I'm still responding to this thread 24 hours later, but just thought I'd add this tweet from Le Cun: "Neuroscience greatly influenced me (there is a direct line from Hubel & Wiesel to ConvNets) and Geoff Hinton.
And the whole idea of neural nets and learning by adjusting synaptic weights clearly comes from neuroscience."
Surely you are trolling me now. There is a very clear biological inspiration mentioned in this paper: they literally define a CNN as having “receptive fields” and then they cite the same Hubel & Wiesel research mentioned before multiple times. LeCun mentions their research in papers even earlier in the 80s as well, during which they were awarded the Nobel prize for their research on the visual system. Of course there is also a lot of computational and mathematical research that was ongoing simultaneously, but to say that there is “no inspiration whatsoever” is pretty far from the truth.
Some time around mid 1995 until basically now, it became out of fashion to explain your motivations of some new modeling as inspired from biology, as that was often handwaving with only little understanding of the actual neuroscience. So that is why people stopped writing that in papers. Just let the actual performance numbers speak for themselves. Either you get good performance, then it doesn't really matter where this was inspired from, or it does not work well, then it also does not matter where this was inspired from. In machine learning, it mostly matters whether it works well or not.
