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Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives

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Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

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.
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Challenging Mathematical Problems With Elementary Solutions (Volume 2)
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Packed with more than 400 techniques, this book delivers what you need to know—on the spot.
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