Learning and Computational Neuroscience: Foundations of Adaptive Networks

Abstract

Learning and Computational Neuroscience presents recent advances in understanding the brain processes underlying learning and memory, including neural systems analyses of dynamic circuit interactions in the brain and computational models capable of describing simple forms of learning and performance. Its principal aim is to show how each approach is related to and benefits the other, providing a powerful strategy for understanding cognitive processes.Michael Gabriel is Professor of Psychology at the University of Illinois. John Moore is Professor of Psychology and Associate Professor of Computer and Information Science at the University of Massachusetts at Amherst.Contributors: Michael Gabriel and John Moore. Joseph E. LeDoux, Bruce S. Kapp, Amy Wilson, Jeffrey P. Pascoe, William Supple, Paul J. Whalen, Norman W. Weinberger, John H. Ashe, Raju Metherate, David M. Diamond, Jon S. Bakin, J. Michael Cassady. Nestor A. Schmajuk. Malcolm W. Brown. Theodore W. Berger, German Barri onuevo, Steven P. Levitan, Donald N. Krieger, Robert J. H. Sclabassi. Neil E. Berthier, Diana E. J. Blazis. E. James Kehoe. John E. Desmond. A. Harry Klopf, James S. Morgan. Richard S. Sutton, Andrew G. Barto. Christopher J. C. H. Watkins.

Keywords

Related Publications