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With the enumerated characteristics as above, neurocybernetics becomes an inevitable subset of biological cybernetics a global control and communication theory as applied to the animal as a whole. Therefore cybernetic attributions to the nervous system forerun their extension to the universality of biological macrocosm. In the framework of cybernetics, the neural functions depicted in terms of control and communication activities could be expanded in a more general sense by enclaving the modern C3I (Command, Communication, Control, and Information) concepts of system management. Such an approach could address the cognitive functions involved in decision-making, planning, and control by the neural intelligence service through its synchronous, nonlinear synaptic agencies often functioning under uncertainties. Yet, it could sustain the scope of machine-intelligence engineering of the neural complex with the possibility of developing artificial neural networks which could mimic and pose the machine-intelligence compatible with that of real neurons. How should neural activities be modeled via cybernetics? The answer to this question rests on the feasibilities of exploring the neural machine intelligence from the viewpoint of neurofunctional characteristics enumerated before. Essentially, the neural complex equates to the cybernetics of estimating input-output relations. It is a self-organizing, trainable-to-learn dynamic system. It encodes (sampled) information in a framework of parallel-distributed interconnected networks with inherent feedback(s); and it is a stochastical system. To portray the neural activity in the cybernetic perspectives, the following family of concepts is raised:
1.4 Statistical Mechanics-Cybernetics-Neural ComplexThough the considerations of statistical mechanics and cybernetic principles as applied to neural networks superficially appear to be disjointed, there is however, a union in their applicability it is the stochastical consideration associated with the interacting neurons. The magnetic-spin analogy based on statistical mechanics models the interacting neurons and such interactions are governed by the principles of statistics (as in magnetic spin interactions). When considering the optimal control strategies involved in self-organizing neurocybernetic processes, the statistics of the associated randomness (being counteracted by the control strategies) plays a dominant role. Further, in both perspectives of statistical mechanics as well as cybernetics, the concepts of entropy and energy relations govern the pertinent processes involved. In view of these facts, the intersecting subsets of the neural complex are illustrated in Figures 1.2 and 1.3.
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