Neuro-informatics and Neural Modelling, Volume 4

1st Edition

Editors: F. Moss S. Gielen
Hardcover ISBN: 9780444502841
eBook ISBN: 9780080537429
Imprint: North Holland
Published Date: 26th June 2001
Page Count: 1080
495.00 + applicable tax
300.00 + applicable tax
375.00 + applicable tax
Unavailable
Compatible Not compatible
VitalSource PC, Mac, iPhone & iPad Amazon Kindle eReader
ePub & PDF Apple & PC desktop. Mobile devices (Apple & Android) Amazon Kindle eReader
Mobi Amazon Kindle eReader Anything else

Institutional Access


Table of Contents

General Preface. Preface to volume 4. Contents of volume 4. Contributors to volume 4.

Part A: Biological physics of neurons and neural networks. Stochastic resonance, noise and information in biophysical systems. Electrical stimulation of the somatosensory system I (K.A. Richardson, J.J. Collins). Phase synchronization: from periodic to choatic and noisy (L. Schimansky-Geier, V.S. Anishchenko, A.Neiman). Fluctuations in neural systems: from subcellular to network levels (P. Århem, H. Liljenström). Chaos and the detection of unstable periodic orbits in biological systems. Controlling cardiac arrhythmias: the relevance of nonlinear dynamics (D.J. Christini, K. Hall, J.J. Collins, L. Glass). Controlling the dynamics of cardiac muscle using small electrical stimuli (D.J. Gauthier, S. Bahar, G.M. Hall). Synchronization. Intrinsic noise from voltage-gated ion channels: effects on dynamics and reliability in intrinsically oscillatory neurons (J.A. White, J.S. Haas). Phase synchronization: from theory to data analysis (M. Rosenblum, A. Pikovsky, et al.). Self organized critically in biophysical applications. Statistical analysis and modeling of calcium waves in healthy and pathological astrocyte syncytia (P. Jung, A.H. Cornell-Bell, et al.)

Part B: Statistical and nonlinear dynamics in neuroscience. Biophysical models for biological neurons. Neurones as physical objects: structure, dynamics and function (H.J. Kappen). Statistical mechanics of recurrent neural networks I - statistics (A.C.C. Coolen). Statistical mechanics of recurrent neural networks II - dynamics (A.C.C. Coolen). Topologically ordered neural networks (J.A. Flanagan). Learning in neural networks Geometry of neural networks: natural gradient for learning (K. Fukumizu). Theory of s


Description

How do sensory neurons transmit information about environmental stimuli to the central nervous system? How do networks of neurons in the CNS decode that information, thus leading to perception and consciousness? These questions are among the oldest in neuroscience. Quite recently, new approaches to exploration of these questions have arisen, often from interdisciplinary approaches combining traditional computational neuroscience with dynamical systems theory, including nonlinear dynamics and stochastic processes. In this volume in two sections a selection of contributions about these topics from a collection of well-known authors is presented. One section focuses on computational aspects from single neurons to networks with a major emphasis on the latter. The second section highlights some insights that have recently developed out of the nonlinear systems approach.


Details

No. of pages:
1080
Language:
English
Copyright:
© North Holland 2001
Published:
Imprint:
North Holland
eBook ISBN:
9780080537429
Hardcover ISBN:
9780444502841

Reviews

How do sensory neurons transmit information about environmental stimuli to the central nervous system? How do networks of neurons in the CNS decode that information, thus leading to perception and consciousness? These questions are among the oldest in neuroscience. Quite recently, new approaches to exploration of these questions have arisen, often from interdisciplinary approaches combining traditional computational neuroscience with dynamical systems theory, including nonlinear dynamics and stochastic processes. In this volume in two sections a selection of contributions about these topics from a collection of well-known authors is presented. One section focuses on computational aspects from single neurons to networks with a major emphasis on the latter. The second section highlights some insights that have recently developed out of the nonlinear systems approach.


About the Editors

F. Moss Editor

Affiliations and Expertise

University of Missouri at St. Louis, St. Louis, MO 63121, USA

S. Gielen Editor

Affiliations and Expertise

Department of Medical Physics and Biophysics, University of Nijmegen, 6525 EZ Nijmegen, The Netherlands