Items related to An Introduction To Neural Networks
Synopsis
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 from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind 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. Table of Contents 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. Represen-tation of Information. Applications of Simple Associators: Concept 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.
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