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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:

  The functional aspects of neurocybernetics are mediated solely by the passage of electrical impulses across neuronal cells.
  From a cybernetics point of view, neuronal activity or the neural network is rooted in mathematics and logic with a set of decision procedures which are typically machine-like.
  Neurocybernetics refers to a special class of finite automata,* namely, those which “learn from experience”.

*Finite Automata : These are well-defined systems capable of being in only a finite number of possible states constructed according to certain rules.
  In the effective model portrayal of a neuronal system, the hardware part of it refers to the electrical or electronic model of the neuro-organismic system involved in control and communication. Classically, it includes the models of Uttley [21], Shannon [22], Walter [23], Ashby [24], and several others. The computer (digital or analog) hardware simulation of the effective models of neurocybernetics could also be classified as a hardware approach involving a universal machine programmed to simulate the neural complex.
  The cybernetic approach of neural networks yields effective models — the models in which “if a theory is stated in symbols or mathematics, then it should be tantamount to a blueprint from which hardware could always be constructed” [25].
  The software aspect of effective models includes algorithms, computer programs; finite automata; information theory and its allied stochastical considerations; statistical physics and thermodynamics; and specific mathematical tools such as game theory, decision theory, boolean algebra, etc.
  The neurocybernetic complex is a richly interconnected system which has inherent self-organizing characteristics in the sense that “the system changes its basic structure as a function of its experience and environment”.
  The cognitive faculties of neurocybernetics are learning and perception. The functional weights on each neuron change with time in such a way as to “learn”. This is learning through experience which yields the perceptive attribution to the cognitive process. What is learned through past activity is memorized. This reinforcement of learned information is a storage or memory feature inherent to neurocybernetic systems.
  Homeostasis considerations of cybernetics in the self-organization procedure are applied through random search or selection of information from a noise-infested environment as perceived in a neural complex.
  The entropical and information-theoretic aspects of the neural complex are evaluated in terms of cybernetic principles.

1.4 Statistical Mechanics-Cybernetics-Neural Complex

Though 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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