An
artificial neuron is a mathematical
function conceived as a
model of biological
neurons. Artificial neurons are the constitutive units in an
artificial neural network. Depending on the specific model used they may be called a
semi-linear unit,
Nv neuron,
binary neuron,
linear threshold function, or
McCulloch–Pitts (MCP) neuron. The artificial neuron receives one or more inputs (representing
dendrites) and sums them to produce an output (representing a neuron's
axon). Usually the sums of each node are weighted, and the sum is passed through a
non-linear function known as an
activation function or
transfer function. The transfer functions usually have a
sigmoid shape, but they may also take the form of other non-linear functions,
piecewise linear functions, or step functions. They are also often
monotonically increasing,
continuous,
differentiable and
bounded.