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The artificial neuron (also called "node") is the basic unit of an artificial neural network, simulating a biological neuron. It receives one or more inputs, sums these, and produces an output after passing the sum through a (usually) non-linear function known as an activation or transfer function. The canonical form of this function is a sigmoid, but may also be another non-linear function, a piecewise linear function, or a step function. Generally, transfer functions are monotonically increasing. An artificial neural network (ANN), also called a simulated neural network (SNN) or just a neural network (NN), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. ...
Neurons (also spelled neurones or called nerve cells) are the primary cells of the nervous system. ...
The terms sigmoid function or sigmoid curve often refer to a special case of the logistic function. ...
To do: 20th century mathematics chaos theory, fractals Lyapunov stability and non-linear control systems non-linear video editing See also: Aleksandr Mikhailovich Lyapunov Dynamical system External links http://www. ...
In mathematics, a function f(x) of a real number variable x is defined piecewise, if f(x) is given by different expressions on various intervals. ...
In mathematics, functions between ordered sets are monotonic (or monotone) if they preserve the given order. ...
Basic structure
For a given artificial neuron, let there be m inputs with signals x1 through xm and weights w1 through wm.
The output of the neuron k is y:
 Where  (Phi) is the activation (or transfer) function.
 Wikipedia does not have an article with this exact name. ...
The output propagates to the next layer (through a weighted synapse) or finally exits the system as part or all of the output.
History Warren McCulloch (November 16, 1899 - September 24, 1969) was an American neurophysiologist and cybernetician. ...
Walter Pitts (1923? - 1969) was a logician who worked in the field of cognitive psychology. ...
1943 (MCMXLIII) is a common year starting on Friday. ...
The perceptron is a type of artificial neural network invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. ...
Types of transfer functions The transfer function of a neuron is chosen to have a number of properties which either enhance or simplify the network containing the neuron. Crucially, for instance, any multi-layer perceptron using a linear transfer function has an equivalent single-layer network; a non-linear function is therefore necessary to gain the advantages of a multi-layer network. An artificial neural network (ANN), also called a simulated neural network (SNN) or just a neural network (NN), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. ...
Step function The output y of this transfer function is binary, depending on whether the input meets a specified threshold, θ. The "signal" is sent, i.e. the output is set to one, if the activation meets the threshold.  See: Step function A function on the reals is a step function if it can be written as a finite linear combination of semi-open intervals. ...
Sigmoid A fairly simple non-linear function, the sigmoid also has an easily calculated derivative, which is used when calculating the weight updates in the network. It thus makes the network more easily manipulable mathematically, and was attractive to early computer scientists who needed to minimise the computational load of their simulations.
See: Sigmoid function The terms sigmoid function or sigmoid curve often refer to a special case of the logistic function. ...
Bibliography - McCulloch, W. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7:115 - 133.
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