Computational Neuroscience
The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to captur
- PDF / 8,810,233 Bytes
- 370 Pages / 492.293 x 751.202 pts Page_size
- 14 Downloads / 242 Views
Wanpracha Chaovalitwongse Panos M. Pardalos Petros Xanthopoulos (Editors)
Computational Neuroscience 13
38
COMPUTATIONAL NEUROSCIENCE
Springer Optimization and Its Applications VOLUME 38 Managing Editor Panos M. Pardalos (University of Florida) Editor–Combinatorial Optimization Ding-Zhu Du (University of Texas at Dallas) Advisory Board J. Birge (University of Chicago) C.A. Floudas (Princeton University) F. Giannessi (University of Pisa) H.D. Sherali (Virginia Polytechnic and State University) T. Terlaky (McMaster University) Y. Ye (Stanford University)
Aims and Scope Optimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics and other sciences. The Springer Optimization and Its Applications series publishes undergraduate and graduate textbooks, monographs and state-of-the-art expository works that focus on algorithms for solving optimization problems and also study applications involving such problems. Some of the topics covered include nonlinear optimization (convex and nonconvex), network flow problems, stochastic optimization, optimal control, discrete optimization, multiobjective programming, description of software packages, approximation techniques and heuristic approaches.
For other titles in this series, go to www.springer.com/series/7393
Computational Neuroscience
By WANPRACHA CHAOVALITWONGSE Rutgers University, Piscataway, NJ, USA PANOS M. PARDALOS University of Florida, Gainesville, FL, USA PETROS XANTHOPOULOS University of Florida, Gainesville, FL, USA
123
Editors Wanpracha Chaovalitwongse Department of Industrial and Systems Engineering Rutgers State University of New Jersey 96 Frelinghuysen Rd. Piscataway NJ 08854 USA [email protected]
Panos M. Pardalos Department of Industrial and Systems Engineering University of Florida 303 Weil Hall Gainesville FL 32611-6595 USA pardalos@ufl.edu
Petros Xanthopoulos Department of Industrial and Systems Engineering University of Florida 303 Weil Hall P.O.Box 116595 Gainesville FL 32611-6595 USA petrosx@ufl.edu
ISSN 1931-6828 ISBN 978-0-387-88629-9 e-ISBN 978-0-387-88630-5 DOI 10.1007/978-0-387-88630-5 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010920236 Mathematics Subject Classification (2000): 92-08, 92C55 c Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with revi
Data Loading...
