This text seeks to describe the types of computation that can be performed by biologically plausible neural networks, and to show how these may be implemented in different systems in the brain. Suitable for researchers, graduate students and advanced undergraduates in the fields of neuroscience and artificial intelligence, this is an accessible introduction to the problems of how the brain works and how our behaviour is produced.
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
3 halftones, 134 line figures, bibliography
ISBN-13
978-0-19-852433-5 (9780198524335)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Klassifikation
Pattern association memory; autoassociation memory; competitive networks, including self-organizing maps; error-correcting networks - perceptrons, backpropagation of error in multilayer networks, and reinforcement learning algorithms; hippocampus and memory; pattern association in the brain - amygdala and orbitofrontal cortex; cortical networks for invariant pattern recognition; motor systems - cerebellum and basal ganglia; cerebral neocortex. Appendix 1: introduction to linear algebra for neural networks. Appendix 2: Information theory. Appendix 3: Pattern associators. Appendix 4: Autoassociators. Appendix 5: Recurrent dynamics.