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Description :
An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks both from a broad neuroscience and cognitive science perspective, with an increased emphasis on the biology and psychology governing the assumptions of the models as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author’s own ideas.
Content :
Introduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Representation of Information. Applications of Simple Associators: Concepts Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann Machines. Nearest Neighbor Models. Adaptive maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network. Afterword. Index. Related Books : | |
 | | MICROSOFT SHAREPOINT 2013 ADMINISTRATION INSIDE OUT
By WILLIAMS, RANDY, CALLAHAN, CA , GIVENS, CHRIS, GROSS, JOHN MILAN, ALDERMAN, BRIAN, BARRERA, JAVIER | |
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