DocumentCode :
770887
Title :
Neural networks in computational science and engineering
Author :
Cybenko, George
Author_Institution :
Dept. of Eng., Dartmouth Coll., Hanover, NH, USA
Volume :
3
Issue :
1
fYear :
1996
Firstpage :
36
Lastpage :
42
Abstract :
An artificial neural network (ANN) is a computational system inspired by the structure, processing method and learning ability of a biological brain. In a commonly accepted model of the brain, a given neuron receives electrochemical input signals from many neurons through synapses-some inhibitory, some excitatory-at its receiving branches, or dendrites. If and when the net sum of the signals reaches a threshold, the neuron fires, transmitting a new signal through its axon, across the synapses to the dendrites of the many neurons it is in turn connected with. In the artificial system, “neurons”, essentially tiny virtual processors, are usually implemented in software. Given an input, an artificial neuron uses some function to compute an output. As the output signal is propagated to other neurons, it is modified by “synaptic weights” or inter-neuron connection strengths. The weights determine the final output of the network, and can thus be adjusted to encode a desired functionality
Keywords :
brain models; neural nets; artificial neural networks; axon; brain model; computational engineering; computational science; dendrites; electrochemical input signals; excitatory synapses; functionality encoding; inhibitory synapses; interneuron connection strengths; neuron firing; output signal propagation; signal sum threshold; software-implemented neurons; synaptic weights; virtual processors; Artificial neural networks; Biological information theory; Biological neural networks; Biological system modeling; Biology computing; Brain modeling; Computer networks; Fires; Neural networks; Neurons;
fLanguage :
English
Journal_Title :
Computational Science & Engineering, IEEE
Publisher :
ieee
ISSN :
1070-9924
Type :
jour
DOI :
10.1109/99.486759
Filename :
486759
Link To Document :
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