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TopNeural Network Model
A brief overview of neural networks is given. In the early 1940s, the pioneers of the field, McCulloch and Pitts, proposed a computational model based on a simple neuron-like element (McCulloch & Pitts, 1943). Since then, various types of neurons and neural networks have been developed independently of their direct similarity to biological neural networks. They can now be considered as a powerful branch of present science and technology.
Neurons are the atoms of neural computation. Out of those simple computational neurons all neural networks are build up. An illustration of a (real-valued) neuron is given in Figure 1. The activity of neuron is defined as:
(1)where
is the real-valued weight connecting neuron
n and
m,
is the real-valued input signal from neuron
, and
is the real-valued threshold value of neuron
. Then, the output of the neuron is given by
. Although several types of activation functions
can be used, the most commonly used are the sigmoidal function and the hyperbolic tangent function.