Abstract
Neural networks are a special class of distributed active systems that consist of discrete two-state elements linked by long-range activatory and inhibitory connections. The applicability of these formal models to description of the actual neural net of the brain remains doubtful. They give what is probably no more than a rough sketch of the extremely complex processes involved in the operation of the brain. Nevertheless, one can easily construct artificial active networks with these properties. Furthermore, artificial neural networks can be used to perform analog information processing. To use them in this way, one needs to be able to engineer the networks to give them the desired activity patterns.
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Mikhailov, A.S. (1990). Neural Networks. In: Foundations of Synergetics I. Springer Series in Synergetics, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-97269-0_6
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