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Artificial Neuron
Artificial Neuron

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!!! Overview
[{$pagename}] is a mathematical function conceived as a model of biological [neurons].
[{$pagename}] 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 [{$pagename}] 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.
!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
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* [#1] - [Artificial neuron|Wikipedia:Artificial_neuron|target='_blank'] - based on information obtained 2017-04-21-
This page (revision-1) was last changed on 29-Nov-2024 16:16 by UnknownAuthorTop