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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. The artificial neuron transfer function should not be confused with a linear system's transfer function. == Basic structure == For a given artificial neuron, let there be ''m'' + 1 inputs with signals ''x''0 through ''x''''m'' and weights ''w''0 through ''w''''m''. Usually, the ''x''0 input is assigned the value +1, which makes it a ''bias'' input with ''w''''k''0 = ''b''''k''. This leaves only ''m'' actual inputs to the neuron: from ''x''1 to ''x''''m''. The output of the ''k''th neuron is: : Where (phi) is the transfer function. File:artificial neuron.png The output is analogous to the axon of a biological neuron, and its value propagates to the input of the next layer, through a synapse. It may also exit the system, possibly as part of an output vector. It has no learning process as such. Its transfer function weights are calculated and threshold value are predetermined. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Artificial neuron」の詳細全文を読む スポンサード リンク
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