The Handbook of Brain Theory and Neural Networks, Second Edition

(ed.)
MIT Press (2002)
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Abstract

A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.

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0262011972   9780262011976

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Chapters

Bayesian methods for supervised neural networks.David Barber
Biophysical mechanisms in neuronal modelling.L. Graham
Digital VLSI Neural Networks.Hammerstrom Dan
Fast visual processing and its implications.S. J. Thorpe & M. Fabre-Thorpe
Graphical models: Probabilistic inference.Michael I. Jordan & Yair Weiss
Hebbian learning and neuronal regulation.G. Chechik, D. Horn & E. Ruppin
Hidden markov models and other finite state automata for sequence processing.Hervé Bourlard & Samy Bengio
Philosophical issues in brain theory and connectionism.Andy Clark & Chris Eliasmith
Philosophical issues in brain theory and connectionism.Chris Eliasmith & Andy Clark
Potential fields and neural networks.J. Liu & O. Khatib
Practical connection between potential fields and neural networks.Jiming Liu & Oussama Khatib
Prosthetics, Motor Control.Gerald E. Loeb & Ning Lan
Robot navigation.J. D. R. Millan
Semantic networks.Michael A. Arbib
Speech recognition technology.F. Beaufays, H. Bourlard, Horacio Franco & Nelson Morgan
Spinal cord of lamprey: Generation of locomotor patterns.J. T. Buchanan
Systematicity in connectionist generalization.R. F. Hadley
Vertebrate locomotion.Auke Jan Ijspeert
Visual Processing of Object Structure.Shimon Edelman & Nathan Intrator
Arm and hand movement control.  (pages 2--110)Stefan Schaal
Biologically inspired robotics.  (pages 2--160)Noel E. Sharkey
Emotional circuits and computational neuroscience.  (pages 2)Jean-Marc Fellous, Jorge L. Armony & Joseph E. LeDoux
Evolution and learning in neural networks.  (pages 2--415)S. Nolfi & D. Parisi
Graphical models: parameter learning.  (pages 2--486)Zoubin Ghahramani
Integrate-and-fire neurons and networks.  (pages 2--577)Wulfram Gerstner
Learning robot control.  (pages 2--983)Stefan Schaal
Sensory systems.  (pages 66--67)M. A. Arbib
Adaptative Resonance Theory.  (pages 87)Gail Carpenter & Stephen Grossberg
Analogy-based reasoning and metaphor.  (pages 106--109)Dedre Gentner & Arthur B. Markman
Biophysical mechanisms in neuronal modeling.  (pages 164--169)Lyle Borg-Graham
Concept learning.  (pages 234--238)Tom J. Palmeri & David Noelle
Convolutional Networks for Images, Speech, and Time Series.  (pages 255--258)Yann LeCun & Yoshua Bengio
Cortical population dynamics and psychophysics.  (pages 294--300)Udo A. Ernst & Christian W. Eurich
Ensemble learning.  (pages 405--408)Thomas G. Dietterichl
Hebbian synaptic plasticity, comparative and developmental aspects.  (pages 515--521)Yves Frégnac
Helmholtz machines and sleep-wake learning.  (pages 522--525)P. Dayan
Imitation.  (pages 566--569)A. Billard
Locomotion, vertebrate.  (pages 649--654)A. Ijspeert
Muscle models.  (pages 711--715)T. G. Sandercock, D. C. Lin & W. Z. Rymer
Optimal sensory encoding.  (pages 815--919)L. Zhaoping
Past tense learning.  (pages 848--851)Amit Almor
Post-Hebbian learning algorithms.  (pages 898--901)P. Érdi & Z. Somogyvári
Sensorimotor interactions: Principles derived from central pattern generators.  (pages 918--922)A. H. Cohen & D. L. Boothe
Statistical mechanics of learning: Generalization.  (pages 922--925)Manfred Opper
Prosthetics, sensory systems.  (pages 926--929)Gerald E. Loeb & B. S. Wilson
Recurrent networks: learning algorithms.  (pages 955--960)Kenji Doya
Robot arm control.  (pages 979--983)C. Torras
Sensorimotor learning.  (pages 1020--1023)D. M. Wolpert & J. R. Flanagan
Sensory coding and information transmission.  (pages 1023--1026)John Hertz & Stefano Panzeri
Sequence learning.  (pages 1027--1030)P. Dominey
Speech production.  (pages 1072--1076)Dani Byrd & Elliot Saltzman
Kinetic models for synaptic interactions.  (pages 1126--1130)Alain Destexhe, Zachary F. Mainen & T. Sejnowski
Neural networks as universal approximators.  (pages 1180--1183)V. Kurková
Visual course control in flies.  (pages 1205--1210)A. Borst & M. Dickinson
Learning and generalization: theoretical bounds.  (pages 3140--3150)Ralf Herbrich & Robert C. Williamson

Similar books and articles

Potential fields and neural networks.J. Liu & O. Khatib - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
Practical connection between potential fields and neural networks.Jiming Liu & Oussama Khatib - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
Digital VLSI Neural Networks.Hammerstrom Dan - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
Evolution and learning in neural networks.S. Nolfi & D. Parisi - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press. pp. 2--415.
Neural networks as universal approximators.V. Kurková - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press. pp. 1180--1183.
Semantic networks.Michael A. Arbib - 2002 - In The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
Bayesian methods for supervised neural networks.David Barber - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
Imitation.A. Billard - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press. pp. 566--569.
Recurrent networks: learning algorithms.Kenji Doya - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press. pp. 955--960.
Ensemble learning.Thomas G. Dietterichl - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press. pp. 405--408.

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Citations of this work

Skilled activity and the causal theory of action.Randolph Clarke - 2010 - Philosophy and Phenomenological Research 80 (3):523-550.

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