Neural Networks: A Systematic Introduction
by Raul Rojas
Publisher: Springer 1996
Number of pages: 509
Theoretical laws and models scattered in the literature are brought together in this book into a general theory of artificial neural nets. Starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. The book for readers who seek an overview of the field and wish to deepen their knowledge. Suitable as a basis for university courses in neurocomputing.
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by Milan Hajek - University of KwaZulu-Natal
Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).
by Eugene M. Izhikevich, at al. - Scholarpedia
Neuroscience, Electrophysiology, Neuron, Network Dynamics, Brain Models, Synapse, Memory, Conditioning, Consciousness, Vision, Olfaction, Neuroimaging, Dynamical Systems, Oscillators, Synchronization, Pattern Formation, Chaos, Bifurcations, etc.
by Rolf Pfeifer, Dana Damian, Rudolf Fuchslin - University of Zurich
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, etc.
by Alex Pappachen James (ed.) - InTech
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: 1) Devices, 2) Models and 3) Applications. Various memristor models are discussed.