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It is evident that the fields that intersect with the global neural complex functions have cross-linked attributes manifesting as unions in the Venn plane. Pertinent to such unions, the vital roots of models which have been developed for the purpose of signifying the functions of real and/or artificial neurons are embodiments of mathematics, biology (or physioanatomy), physics, engineering, and computer and informational sciences. This book delves in to the generalities of the above faculties of science, but largely adheres to statistical mechanics which deals with global properties of a large number of interacting units and cybernetics which are concerned with complex systems with constrained control efforts in seeking a self-regulation on system disorganization.


Figure 1.2  Overlaps of neural complex-related sciences

The reasons for the bifaceted (statistical mechanics and cybernetics) perspectives adopted in this book for neural network modeling stem from the sparse treatment in the literature in portraying the relevant physical concepts (especially those of cybernetics) in describing the neural network complexities. Further, the state-of-the-art informatic considerations on neural networks refer mostly to the memory and information processing relation between the neural inputs and the output; but little has been studied on the information or entropy relations pertinent to the controlling endeavors of neural self-regulation. An attempt is therefore made (in the last chapter) to present the salient aspects of informatic assays in the neurocybernetic perspectives. Collectively, the theoretical analyses furnished are to affirm the capability of the neural networks and indicate certain reliable bases for modeling the performance of neural complexes under conditions infested with intra- or extracellular perturbations on the state-transitions across the interconnected neurons.


Figure 1.3  Common bases of neural theory-related sciences

1.5 Concluding Remarks

The strength of physical modeling of a neural complex lies in a coherent approach that accounts for both stochastical considerations pertinent to interacting cells and self-regulatory features of neurocybernetics. The mediating process common to both considerations is the entropy or the informational entity associated with the memory, computational, and self-controlling efforts in the neural architecture. This book attempts to address the missing links between the aforesaid considerations, the broad characteristics of which are outlined in this chapter.


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