McCulloch-Pitts neuron

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McCulloch-Pitts neuron

(artificial intelligence)
The basic building block of artificial neural networks. It receives one or more inputs and produces one or more identical outputs, each of which is a simple non-linear function of the sum of the inputs to the neuron. The non-linear function is typically a threshhold or step function which is usually smoothed (i.e. a sigmoid) to facilitate learning.
References in periodicals archive ?
The abstract mathematical description of an artificial neuron (Fig.
The basic element in the NN is a processing element, called an artificial neuron or node.
We could replace a damaged nerve with an artificial neuron and restore functionality immediately, and that's a really exciting possibility.
An artificial neuron is a model of a biological neuron.
Figure 1 shows a fundamental representation of an artificial neuron, simulating the four basic functions of a natural neuron.
Among his topics are the artificial neuron, model testing, the fuzzy logic algorithm, constructing a fuzzy model, and a variant of a genetic algorithm.
An artificial neuron is a computational model inspired in the natural neurons receiving signals through synapses located on dendrites.
In order to allow the application of these methods, it is necessary to supplement the artificial neuron network model with a weight changing processor and error detector.
However, the artificial neuron model has been expanded to include other functions such as the sigmoid, piecewise linear, and Gaussian functions.
The hyperbolic tangent creates non-linear dependence between input and output data of the artificial neuron (Briliuk, Starovoitov 2002).

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