This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the gap between present neural network models and the needs of neurophysiologists who are trying to use these models as part of their research on how the brain works.
Contents
Introduction: Toward Neural Neural Networks, Daniel Gardner • Two Principles of Brain Organization: A Challenge for Artificial Neural Networks, Charles F. Stevens • Static Determinants of Synaptic Strength, Daniel Gardner • Learning Rules From Neurobiology, Douglas A. Baxter and John H. Byrne • Realistic Network Models of Distributed Processing in the Leech, Shawn R. Lockery and Terrence J. Sejnowski • Neural and Peripheral Dynamics as Determinants of Patterned Motor Behavior, Hillel J. Chiel and Randall D. Beer • Dynamic Neural Network Models of Sensorimotor Behavior, Eberhard E. Fetz