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Chapter 1
Introduction

1.1 General

The interconnected biological neurons and the network of their artificial counterparts have been modeled in physioanatomical perspectives, largely via cognitive considerations and in terms of physical reasonings based on statistical mechanics of interacting units. The overall objective of this book is to present a cohesive and comprehensive compendium elaborating the considerations of statistical mechanics and cybernetic principles in modeling real (biological) neurons as well as neuromimetic artificial networks. While the perspectives of statistical mechanics on neural modeling address the physics of interactions associated with the collective behavior of neurons, the cybernetic considerations describe the science of optimal control over complex neural processes. The purpose of this book is, therefore, to highlight the common intersection of statistical mechanics and cybernetics with the universe of the neural complex in terms of associated stochastical attributions.

In the state-of-the-art data-processing systems, neuromimetic networks have gained limited popularity largely due to the fragmentary knowledge of neurological systems which has consistently impeded the realistic mathematical modeling of the associated cybernetics. Notwithstanding the fact that modern information processing hinges on halfway adoption of biological perspectives on neurons, the concordant high-level and intelligent processing endeavors are stretched through the self-organizing architecture of real neurons. Such architectures are hierarchically structured on the basis of interconnection networks which represent the inherent aspects of neuronal interactions.

In order to sway from this pseudo-parasitical attitude, notionally dependent but practically untied to biological realities, the true and total revolution warranted in the application-based artificial neurons is to develop a one-to-one correspondence between artificial and biological networks. Such a handshake would “smear” the mimicking artificial system with the wealth of complex automata, the associated interaction physics, and the cybernetics of the biological neurons — in terms of information processing mechanisms with unlimited capabilities.

For decades, lack of in-depth knowledge on biological neurons and the nervous system has inhibited the growth of developing artificial networks in the image of real neurons. More impediments have stemmed from inadequate and/or superficial physicomathematical descriptions of biological systems undermining their total capabilities — only to be dubbed as totally insufficient for the requirements of advances in modern information processing strategies.

However, if the real neurons and artificial networks are viewed through common perspectives via physics of interaction and principles of cybernetics, perhaps the superficial wedlock between the biological considerations and artificial information processing could be harmonized through a binding matrimony with an ultimate goal of realizing a new generation of massively parallel information processing systems.

This book is organized to elucidate all those strands and strings of biological intricacies and suggest the physicomathematical modeling of neural activities in the framework of statistical mechanics and cybernetic principles. Newer perspectives are projected for the conception of better artificial neural networks more akin to biological systems. In Section 1.2, a broad outline on the state-of-the-art aspects of interaction physics and stochastical perspectives of the neural system is presented. A review on the relevant implications in the information processing is outlined. Section 1.3 introduces the fundamental considerations in depicting the real (or the artificial) neural network via cybernetic principles; and the basics of control and self-control organization inherent to the neural system are indicated. The commonness of various sciences including statistical mechanics and cybernetics in relation to complex neural functions is elucidated in Section 1.4; and concluding remarks are furnished in Section 1.5.


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