Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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 biologically-inspired neural networks, the activation function is usually an abstraction representing the rate of action potential firing in the cell. In its simplest form, this function is binary--that is, either the neuron is firing or not. The function looks like (vi) = U(vi), where U is the Heaviside step function. In this case a large number of neurons must beused in computation beyond linear separation of categories.

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