An artificial neuron is a mathematical function conceived as a crude model, or abstraction of biological neurons. Artificial neurons are the constitutive units in an artificial neural network. Depending on the specific model used, it can receive different names, such as semi-linear unit, Nv neuron, binary neuron, linear threshold function or McCulloch–Pitts (MCP) neuron . The artificial neuron receives one or more inputs (representing the one or more dendrites) and sums them to produce an output (representing a biological 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 artificial neuron transfer function should not be confused with a linear system's transfer function.
Read more about Artificial Neuron: Basic Structure, Comparison To Biological Neurons, History, Types of Transfer Functions, Pseudocode Algorithm, Spreadsheet Example, Limitations
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“Kings are not born: they are made by artificial hallucination.”
—George Bernard Shaw (1856–1950)