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Learning and Memory,9781464105937

Learning and Memory

by ; ;
Edition: 3rd
Format: Hardcover
Pub. Date: 2/15/2016
Publisher(s): Worth Publishers

Author Biography

Mark A. Gluck is a Professor of Neuroscience at Rutgers University–Newark, co-director of the Memory Disorders Project at Rutgers–Newark, and publisher of the project’s public health newsletter, Memory Loss and the Brain.  His research focuses on the neural bases of learning and memory, and the consequences of memory loss due to aging, trauma, and disease.  He is co-author of Gateway to Memory: An Introduction to Neural Network Modeling of the Hippocampus and Learning (MIT Press, 2001).  In 1996, he was awarded an NSF Presidential Early Career Award for Scientists and Engineers by President Bill Clinton. That same year, he received the American Psychological Association (APA) Distinguish Scientific Award for Early Career Contribution to Psychology.
 
Eduardo Mercado is an Assistant Professor of Psychology at University at Buffalo, The State University of New York.  His research focuses on how different brain systems interact to develop representations of experienced events, and how these representations change over time.  Dr. Mercado currently uses techniques from experimental psychology, computational neuroscience, electrical engineering, and behavioral neuroscience to explore questions about auditory learning and memory in rodents, cetaceans, and humans.
Catherine E. Myers is a Research Scientist with the Department of Veterans Affairs, New Jersey Health Care System, and a Professor in the department of Pharmacology, Physiology, and Neuroscience at the New Jersey Medical School, Rutgers University.  Her research includes both computational neuroscience and experimental psychology, and focuses on human learning and memory, especially in clinical disorders such as amnesia and post traumatic stress disorder (PTSD).  She is co-author of Gateway to Memory: An Introduction to Neural Network Modeling of the Hippocampus and Learning (MIT Press, 2001) and author of Delay Learning in Artificial Neural Networks (Chapman and Hall, 1992).

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