In
computational networks, the
activation function of a node defines the output of that node given an input or set of inputs. A standard
computer chip circuit can be seen as a
digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. This is similar to the behavior of the
linear perceptron in
neural networks. However, it is the
nonlinear activation function that allows such networks to compute nontrivial problems using only a small number of nodes. In artificial neural networks this function is also called
transfer function (not to be confused with a linear system’s
transfer function).