Neural Network Design
by Martin T. Hagan, et al.
Number of pages: 1012
This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.
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by B. Mehlig - arXiv.org
These are lecture notes for my course on Artificial Neural Networks. This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning algorithms.
by Todd Troyer - University of Texas at San Antonio
These notes have three main objectives: to present the major concepts of computational neuroscience, to present the basic mathematics that underlies these concepts, and to give the reader some idea of common approaches taken by neuroscientists.
by Raul Rojas - Springer
A general theory of artificial neural nets. The book starts with the simple nets, and shows how the models change when more general computing elements and net topologies are introduced. Suitable as a basis for university courses in neurocomputing.
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.