The backpropagation neural network itself is a variation of many nonlinear, additive statistical models. The field emerged as an independent entity largely because it had "cool" neurological connotations and was largely ignored by statisticians. The Netflix prize was ultimately won using linear algebra with sensible starting values (the KDD paper that describes the winning method is remarkably easy to read).

backpropagation is just the chain rule of differentiation. each layer of an ANN is logistic regression, which statisticians have been and continue to be interested in.

i'm not sure your caricature of the netflix winning solution is correct: i believe it was a blending of around 25 different models (including, i think, the RBM someone pointed about above) each in themselves quite varied from one another. this is typically how these challenges are won.

A lot of the top teams had blended submissions, but my characterization was not incorrect: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5197...

I work on the cinematch team at netflix... I've reviewed all of the winning code... your characterization is incorrect.

The netflix prize would not have been won when it was without rbm.

Is http://www.commendo.at/references/files/kdd08.pdf (this) the paper you are talking about?

Nope, it was by Bell and Koren.

This one is related, but not the one I originally mentioned: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5197...

I was at the talk but can't for the life of me remember the title, sorry.

not an expert either, and indeed, the Hinton papers on RBMs are the last thing i remember reading about. But their invention dates back to the 80s. I 'm just noticing that if you search "neural network" on google scholar for the recent years, you will find many neuroscientific results. Of course i dont have the data to back it up.