An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary unit of an artificial neural network.

The design of the artificial neuron was inspired by biological neural circuitry. Its inputs are analogous to excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites, or activation. Its weights are analogous to synaptic weights, and its output is analogous to a neuron's action potential which is transmitted along its axon.

Usually, each input is separately weighted, and the sum is often added to a term known as a bias (loosely corresponding to the threshold potential), before being passed through a nonlinear function known as an activation function. Depending on the task, these functions could have a sigmoid shape (e.g. for binary classification), but they may also take the form of other nonlinear functions, piecewise linear functions, or step functions. They are also often monotonically increasing, continuous, differentiable, and bounded. Non-monotonic, unbounded, and oscillating activation functions with multiple zeros that outperform sigmoidal and ReLU-like activation functions on many tasks have also been recently explored. The threshold function has inspired building logic gates referred to as threshold logic; applicable to building logic circuits resembling brain processing. For example, new devices such as memristors have been extensively used to develop such logic.

The artificial neuron activation function should not be confused with a linear system's transfer function.

An artificial neuron may be referred to as a semi-linear unit, Nv neuron, binary neuron, linear threshold function, or McCulloch–Pitts (MCP) neuron, depending on the structure used.

Simple artificial neurons, such as the McCulloch–Pitts model, are sometimes described as "caricature models", since they are intended to reflect one or more neurophysiological observations, but without regard to realism. Artificial neurons can also refer to artificial cells in neuromorphic engineering that are similar to natural physical neurons.

For a given artificial neuron ${\displaystyle k}$, let there be ${\displaystyle m+1}$ inputs with signals ${\displaystyle x_{0}}$ through ${\displaystyle x_{m}}$ and weights ${\displaystyle w_{k0}}$ through ${\displaystyle w_{km}}$. Usually, the input ${\displaystyle x_{0}}$ is assigned the value +1, which makes it a bias input with ${\displaystyle w_{k0}=b_{k}}$. This leaves only ${\displaystyle m}$ actual inputs to the neuron: ${\displaystyle x_{1}}$ to ${\displaystyle x_{m}}$.

The output of the ${\displaystyle k}$-th neuron is: