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.
This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)
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Figure 2 shows a representation of a biological neuron (A), artificial neuron (B) and the architecture of artificial neural networks (C).
Japkowicz explains how an artificial neural network works: Information gets transmitted from artificial neuron to artificial neuron through highly parallel connections that get stronger and stronger as similar patterns are observed.
artificial neuron. While originally based on a simplistic model of the
In an artificial neuron, the information enters the body of an artificial neuron via inputs that are individually weighed.
The basic building block of every artificial neural network is an artificial neuron, that is, a simple mathematical model (function).
According to Oztemel, (2003), "the ANN is a computer based structure, designed to automate (i.e., deprived of the attainment of any assistance) the process of creating, assembling and formatting new evidence through learning, which is one of the characteristics of the human brain." an artificial neuron is a computational model inspired by the complex natural neural system.
Elements of the artificial neuron are represented by: m that indicates the number of the neuron input signals; [x.sub.j] the j-th neuron input signal; [w.sub.gl] the weight associated with the j-th neuron input signal g; b the threshold of each neuron, also called bias; [v.sub.g] a weighted combination of input signals and the g-th neuron bias and [phi](.) as an activation function of the g-th neuron (HAYKIN, 2001).
Digital Hardware Implementation of Artificial Neuron Model Using FPGA.
The scientists and researchers have successfully completed the development of first artificial neuron that is amazingly capable of imitating the working of neuron cell with potential to interpret the chemical signals into electrical signals and report to other cells.

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