• Konstantin V. Baev


We can now make a natural and logical step toward further generalization of the automatism concept. Obviously, it is possible to talk about different automatisms in addition to the automatisms of neural networks. They can be molecular, genetic, biochemical, cellular, and even social. In all these cases, the control system is hierarchical, and each level is an OCS built of a network of interacting elements. Therefore, all of the concepts described in the previous chapters are applicable to non-neuronal automatisms. This conclusion is completely in line with the well-known fact that the same type of computation can be realized by using different types of elements—electronic devices, mechanical devices, chemical reactions, etc. Some examples of non-neuronal networks are discussed below in order to show how such a generalization is broadly applicable. Let us first go back to the process of learning in the nervous system, taking into account the presence of molecular and other non-neuronal automatisms.


Internal Model Control Object Central Pattern Generator Mesencephalic Locomotor Region Antiidiotypic Antibody 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Boston 1998

Authors and Affiliations

  • Konstantin V. Baev
    • 1
  1. 1.Department of Neurosurgery, Barrow Neurological InstituteSt. Joseph’s Hospital and Medical CenterPhoenixUSA

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