Mark Gluck, Rutgers–The State University of New Jersey, Newark Campus
Mark A. Gluck is a Professor of Neuroscience at Rutgers–The State University of New Jersey, Newark Campus, co-director of the Memory Disorders project at Rutgers, and publisher of the project’s information 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.
Eduardo Mercado, University at Buffalo, State University of New York
Eduardo Mercado is Assistant Professor of Psychology at University at Buffalo, 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, Rutgers–The State University of New Jersey, Newark Campus
Catherine E. Myers is a Research Associate Professor in the Psychology Department at Rutgers–The State University of New Jersey, Newark Campus, working in experimental neuropsychology and computational neuroscience. She is co-director of the Memory Disorders project at Rutgers as well as Editor-in-Chief of the project’s information newsletter, Memory Loss and the Brain. Her research focuses on human memory, specifically on memory impairments following damage to the hippocampus and associated brain structures. She is co-author of the Gateway to Memory: An Introduction to Neural Network Modeling of the Hippocampus and Learning (MIT Press, with Mark Gluck) and author of Delay Leaning in Artificial Neural Networks (Chapman and Hall, 1992).
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