The biologically-inspired nature of neural nets was a steady, mesmerizing flame for machine learning research. A chance to work on an extremely simple, provably universal system that had embarrassingly obvious rhetorical implications for profound-sounding problems like the computability of consciousness, or the computational complexity of the human brain, proved too seductive for the legions of research-moths that flocked to bask in that alluring flame.
The initial flurry of research activity slowed to a background simmer, however. As a steady march of implementation problems was knocked down, it became clear that the limitations of neural nets were inherent to neural nets themselves, rather than any particular implementation detail. For example, the Lagrangians calculated by SVMs are provably convex, in contrast to the lack of a convexity guarantee for neural nets, which happily gradient descent into local minima. There were workarounds, of course, with regularization techniques, stochastic descent, etc. providing some measure of relief, but a migration of research interest away from neural nets seemed increasingly promising, and today, the migration seems largely complete.
The initial flurry of research activity slowed to a background simmer, however. As a steady march of implementation problems was knocked down, it became clear that the limitations of neural nets were inherent to neural nets themselves, rather than any particular implementation detail. For example, the Lagrangians calculated by SVMs are provably convex, in contrast to the lack of a convexity guarantee for neural nets, which happily gradient descent into local minima. There were workarounds, of course, with regularization techniques, stochastic descent, etc. providing some measure of relief, but a migration of research interest away from neural nets seemed increasingly promising, and today, the migration seems largely complete.