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 ?
an artificial neuron is a computational model inspired by the complex natural neural system.
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.
Artificial neuron is extremely simple abstraction of biological neuron, implemented as element in a program or perhaps as circuits made of silicon (Guo et al.
The basic building blocks that are necessary for FPGA implementation of artificial neuron are adder, multiplier and nonlinear function that is hardware unfavourable.
The artificial neuron is a simple mapping model; artificial neuron model can be expressed as
The body of the artificial neuron consisted of a sum function (linear combiner [SIGMA]), represented by the weighted sum of the values received by the neuron through the synapses and summed to the bias value that is externally applied and has the effect of increasing or decreasing the output value ([v.
The basic element in the NN is a processing element, called an artificial neuron or node.
The abstract mathematical description of an artificial neuron (Fig.

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