Earlier quoted context omitted.
Current AI approaches don't have that much to do with the biological brain. Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Historically researchers looked at activation functions that are at least somewhat justified by nature, like the sigmoid. More recently people have focused on the math…
> Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Actually, the name is not so misleading because `ReLU(Wx + b)` is an approximation of the response of a large ensemble of integrate-and-fire neurons in terms of the firing rate [1] (it models populations of pyramidal neurons in the cortex).…
(I only mention ReLU because they are definitely simpler in mathematical structure than ye olde sigmoid. Max out is another interesting activation function that's first and foremost inspired by the math / pragmatics of drop-out training, and were any relations to biology more likely to be subsequent discoveries and not intentional design features.)