Artificial neurons

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing dendrites) and sums them to produce an output (or activation) (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. The thresholding function is inspired to build logic gates referred to as threshold logic; with a renewed interest to build logic circuit resembling brain processing. For example, new devices such as memristors have been extensively used to develop such logic in the recent times.

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

For a given artificial neuron, let there be m + 1 inputs with signals x₀ through x_m and weights w₀ through w_m. Usually, the x₀ input is assigned the value +1, which makes it a bias input with w_k0 = b_k. This leaves only m actual inputs to the neuron: from x₁ to x_m.

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