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Neural networks in computational science and engineering

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1 Author(s)
G. Cybenko ; Dept. of Eng., Dartmouth Coll., Hanover, NH, USA

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

Published in:

IEEE Computational Science and Engineering  (Volume:3 ,  Issue: 1 )