Activation Function
Version 1.0.0 · Updated 2026-07-28
CORE DEFINITION
In a computational network, the activation function of a node defines the output of that node given an input or a set of inputs. A standard computer chip circuit can be seen as a digital circuit activation function that produces an output of on (1) or off (0) based on the input. This is similar to the behavior of a linear perceptron in neural networks. However, only nonlinear activation functions allow such networks to compute nontrivial problems using a small number of nodes. In artificial neural networks, this function is also called a transfer function.
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In a computational network, the activation function of a node defines the output of that node given an input or a set of inputs. A standard computer chip circuit can be seen as a digital circuit activation function that produces an output of on (1) or off (0) based on the input. This is similar to the behavior of a linear perceptron in neural networks. However, only nonlinear activation functions allow such networks to compute nontrivial problems using a small number of nodes. In artificial neural networks, this function is also called a transfer function.
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A function that introduces nonlinearity into neural networks, determining whether and to what extent a neuron is "activated" to output, enabling multi-layer networks to approximate any complex function, breaking the limitation of linear superposition.
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- zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0verified
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