Neural Networks and Psychopathology
Connectionist Models in Practice and Research

This book reviews the contribution of neural network models in psychiatry and psychopathology, including diagnosis, pharmacotherapy and psychotherapy.

Dan J. Stein (Edited by), Jacques Ludik (Edited by)

9780521571630, Cambridge University Press

Hardback, published 3 December 1998

386 pages, 53 b/w illus. 6 tables
23.6 x 15.7 x 2.4 cm, 0.74 kg

Research on connectionist models is one of the most exciting areas in cognitive science, and neural network models of psychopathology have immediate theoretical and empirical appeal. The contributors to this study review theoretical, historical and clinical issues, including the contribution of neural network models to diagnosis, pharmacotherapy and psychotherapy. Models are presented for a range of disorders, including schizophrenia, obsessive-compulsive disorder, dissociative phenomena, autism and Alzheimer's disease. This book will appeal to a broad audience. On the one hand, it will be read with interest by psychiatrists, psychologists and other clinicians and researchers in psychopathology. On the other, it will appeal to those working in cognitive science and artificial intelligence, and particularly those interested in neural network or connectionist models.

List of contributors
Preface
Part I. General Concepts: 1. Neural networks and psychopathology: an introduction Dan J. Stein and Jacques Ludik
2. The history of neural network research in psychopathology Manfred Spitzer
3. Neural network models in psychiatric diagnosis and symptom recognition Eric Y. H. Chen and German E. Berrios
4. Neural networks and psychopharmacology S. B. G. Park
5. A connectionist view of psychotherapy Franz Caspar
6. Modulatory mechanisms in mental disorders David Hestenes
Part II. Clinical Disorders: 7. The nature of delusions: a hierarchical neural network approach Eric Y. H. Chen and German E. Berrios
8. 'Produced by either God or Satan': neural network approaches to delusional thinking Sophia Vinogradov, John H. Poole and Jason Willis-Shore
9. Neural network modelling of cognitive disinhibition and neurotransmitter dysfunction in obsessive-compulsive disorder Jacques Ludik and Dan J. Stein
10. The fables of Lucy R.: association and disassociation in neural networks Dan Lloyd
11. Neural network analysis of learning in autism Ira L. Cohen
12. Are there common neural mechanisms for learning, epilepsy and Alzheimer's disease? Gene V. Wallenstein and Michael E. Hasselmo
Epilogue: the patient in the machine: challenges for neurocomputing David V. Forrest
Index.

Subject Areas: Neurology & clinical neurophysiology [MJN]