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Artificial Neural Networks Background , Evolutionary Models and History *

The theory of the artificial neural networks (NNs) is an important division of the artificial intelligence (AI) theory. It reflects the key components of the AI-systems [1]: representation, reasoning and learning. On the other hand, according to [2] the artificial NNs differ from the symbolic AI-machines by the level of explanation, by their processing style and by their representational structure. If the symbolic AI is described in a “top-down” fashion, the NNs have a natural learning capability and they operate in a “bottom-up” manner. If in classical AI the emphasis is on building symbolic representations, which are discrete and arbitrary and the processing is sequential, the NN models are of a parallel distributed processing. In such models the processing takes place through the interaction of a large number of neurons. Each of the neurons sends excitatory or inhibitory signals to other neurons in the network. In addition, NNs place great emphasis on neurobiological explanation of cognitive phenomena. The role of the artificial NNs as a technical counterpart of their biological prototypes is explained in Section 2. If in classical AI the processing is sequential, which most probably can be explained by the sequential nature of natural language and logical inference, and also by the von Neumann’s machine structure, the parallelism in the NNs makes them more flexible and robust. The false switching of a single neuron is not crucial for the whole system, because each feature is spread in parallel to many neurons. The automatic processing of contextual information is another important corollary of parallelism. Knowledge in NNs is not represented by declarative expressions, but by the very structure and activation state of the network. It is the whole NN that is responsible for the solution of a given problem. Every neuron is potentially affected by the global activity of all other neurons in the network and the result is an automatic context handling. The texts most closely related to the NN processing style, but encompassing the representativeness of classical AI (in hybrid models) are cited in Section 3.