Short Review of Artificial Neural Networks

As mentioned in the introduction, ANNs are models inspired by the structure and the functions of the biological neurons, since they can also recognize patterns, disordered structure data and can learn from observation. 

A network is composed of units or simple named nodes, which represent the neuron bodies. These units are interconnected by links that act like the axons and dendrites of their biological counterparts. A particular type of interconnected neural net is shown in Fig. 5.12. In this case, it has one input layer of three units (leftmost circles), a central or hidden layer (five circles) and one output (exit) layer (rightmost) unit. This structure is designed for each particular application, so the number of the artificial neurons in each layer and the number of the central layers is not a priori fixed. 

The system behaves like synaptic connections where each value of a connection is multiplied by a connecting weight and then the obtained value is transferred to another unit, where all the connecting inputs are added. If the total sum exceeds a certain threshold value (also called offset or bias), the neuron begins to fire [5.45, 5.46]. 

The changes brought about in the pattern of neurons constitute the basis for learning. 

In biological neurons, learning is carried out by changing the synaptic resistance associated to a change in the activation pattern of neurons. 

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