As a data scientist dating a neuro-scientist, I can confirm that the two fields are quite apart in practice (except from the occasional image processing of fMRI

As a data scientist dating a neuro-scientist, I can confirm that the two fields are quite apart in practice (except from the occasional image processing of fMRIs) — however, Cybenko’s work started in the late 70s; the inspiration for using a formalism of unit perceptrons organised in layers (as well as the convolution structure) comes from a paper on cat’s vision system published in 1962 [1]. As far as I’m aware, all key papers (Hinton, Le Cunn) cite Hubel & Wiesel as their explicit inspiration. The current practice has moved far from neuro-science, but I don’t think it’s pedantic when discussing machine learning to talk about “artificial neural networks” and acknowledge that neuroscience is still working on understanding the structure of brains. Some people, focused on their research, didn’t have that discretion. I wouldn’t imitate them. [1] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/