[Télécharger] Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency (Tutorial Introductions) de James V Stone Pdf Ebook
Télécharger Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency (Tutorial Introductions) de James V Stone livre En ligne
Télécharger "Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency (Tutorial Introductions)" de James V Stone livre En ligne
Auteur : James V Stone
Catégorie : Livres anglais et étrangers,Computers & Internet,Computer Science
Broché : * pages
Éditeur : *
Langue : Français, Anglais
The brain is the most complex computational machine known to science, even though its components (neurons) are slow and unreliable compared to a laptop computer. In this richly illustrated book, Shannon's mathematical theory of information is used to explore the computational efficiency of neurons, with special reference to visual perception and the efficient coding hypothesis. Evidence from a diverse range of research papers is used to show how information theory defines absolute limits on neural processing; limits which ultimately determine the neuroanatomical microstructure of the eye and brain. Written in an informal style, with a comprehensive glossary, tutorial appendices, and a list of annotated Further Readings, this book is an ideal introduction to the principles of neural information theory.
Télécharger Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency (Tutorial Introductions) de James V Stone Livre PDF Gratuit
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A Basic Introduction To Neural Networks ~ To better understand artificial neural computing it is important to know first how a conventional 'serial' computer and it's software process information. A serial computer has a central processor that can address an array of memory locations where data and instructions are stored. Computations are made by the processor reading an instruction as well as any data the instruction requires from .
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