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Artificial Neuron

Overview#

Artificial Neuron is a mathematical function conceived as a model of biological neurons.

Artificial Neuron 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 (or activation) (representing a neuron's axon). Usually the sums of each node have Weights, and the sum is passed through a non-linear function known as an Activation Function or transfer function. The Activation Functions usually have a sigmoid function 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.

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