Synopses & Reviews
'Reviews the contribution of neural network models in psychiatry and psychopathology, including diagnosis, pharmacotherapy and psychotherapy.'
Synopsis
Research on connectionist models is one of the most exciting areas in cognitive science, and this pioneering book reviews theoretical, historical and clinical issues, including the contribution of neural network models to diagnosis, pharmacotherapy and psychotherapy. It will be read with interest by psychiatrists, psychologists and other clinicians and researchers in psychopathology, and will appeal to those working in cognitive science and artificial intelligence, and particularly those interested in neural network or connectionist models.
Table of Contents
'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. âProduced by either God or Satanâ: 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 Alzheimerâs disease? Gene V. Wallenstein and Michael E. Hasselmo; Epilogue: the patient in the machine: challenges for neurocomputing David V. Forrest; Index.\n
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