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Foreword
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- By Bart Kosko, Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Sergey M. Bezrukov, National Institutes of Health (NIH), Bethesda, Washington DC, USA
- Mark D. McDonnell, Nigel G. Stocks, University of Warwick, Charles E. M. Pearce, University of Adelaide, Derek Abbott, University of Adelaide
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- Book:
- Stochastic Resonance
- Published online:
- 23 October 2009
- Print publication:
- 02 October 2008, pp xvii-xviii
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Summary
Due to the multidisciplinary nature of stochastic resonance the Foreword begins with a commentary from Bart Kosko representing the engineering field and ends with comments from Sergey M. Bezrukov representing the biophysics field. Both are distinguished researchers in the area of stochastic resonance and together they bring in a wider perspective that is demanded by the nature of the topic.
The authors have produced a breakthrough treatise with their new book Stochastic Resonance. The work synthesizes and extends several threads of noise-benefit research that have appeared in recent years in the growing literature on stochastic resonance. It carefully explores how a wide variety of noise types can often improve several types of nonlinear signal processing and communication. Readers from diverse backgrounds will find the book accessible because the authors have patiently argued their case for nonlinear noise benefits using only basic tools from probability and matrix algebra.
Stochastic Resonance also offers a much-needed treatment of the topic from an engineering perspective. The historical roots of stochastic resonance lie in physics and neural modelling. The authors reflect this history in their extensive discussion of stochastic resonance in neural networks. But they have gone further and now present the exposition in terms of modern information theory and statistical signal processing. This common technical language should help promote a wide range of stochastic resonance applications across engineering and scientific disciplines. The result is an important scholarly work that substantially advances the state of the art.
11 - Stochastic resonance and small-amplitude signal transduction in voltage-gated ion channels
- Edited by Jan Walleczek, Stanford University, California
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- Book:
- Self-Organized Biological Dynamics and Nonlinear Control
- Published online:
- 14 August 2009
- Print publication:
- 18 May 2000, pp 257-280
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Summary
Introduction
Voltage-gated ion channels are crucial ‘building blocks’ in various systems of signal transduction and processing in living organisms. They are ultimately responsible for information flow at several hierarchical levels of biological complexity that include signal sensing (Lu and Fishman, 1994) and generation of nerve action potentials (Hille, 1992), and are crucially important in synaptic transmission and other intercellular communications (Alberts et al., 1994). Preceding biologically inspired work on the role of external noise in electrical signal transduction concentrated on rather complex objects such as neurons (Bulsara et al., 1994; Pei et al., 1996; Chapeau-Blondeau et al., 1996; Longtin, 1997; Plesser and Tanaka, 1997) and neuronal ensembles (Gluckman et al., 1996; Chialvo et al., 1997). It was demonstrated that addition of random fluctuations, or noise to the input of these systems could improve the transmission efficiency for small input signals. Yet even more elaborate physiological systems showing similar properties include isolated sciatic nerves of a toad (Morse and Evans, 1996; Moss et al., 1996), rat SA1 cutaneous mechanoreceptors (Collins et al., 1996a), mechanosensory transduction pathways in arthropods (Douglass et al., 1993; Levin and Miller, 1996), and human sensory perception (Cordo et al., 1996; Collins et al., 1996b; Chiou-Tan et al., 1996; Simonoto et al., 1997). The counterintuitive phenomenon of noise-improved signal transduction, called ‘stochastic resonance’ – first introduced as a possible explanation for the periodic recurrences of the Earth's ice ages (Benzi et al., 1981) – has now been established empirically for many macroscopic systems and, for some of them, is understood theoretically (Wiesenfeld and Moss, 1995; Gammaitoni et al., 1998; see also Moss, Chapter 10, this volume).