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.

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


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© 2001
North Holland
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