Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introductory Information Theory and the Brain
- Part One Biological Networks
- Part Two Information Theory and Artificial Networks
- Part Three Information Theory and Psychology
- Part Four Formal Analysis
- 14 Quantitative Analysis of a Schaffer Collateral Model
- 15 A Quantitative Model of Information Processing in CA1
- 16 Stochastic Resonance and Bursting in a Binary-Threshold Neuron with Intrinsic Noise
- 17 Information Density and Cortical Magnification Factors
- Bibliography
- Index
16 - Stochastic Resonance and Bursting in a Binary-Threshold Neuron with Intrinsic Noise
from Part Four - Formal Analysis
Published online by Cambridge University Press: 04 May 2010
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introductory Information Theory and the Brain
- Part One Biological Networks
- Part Two Information Theory and Artificial Networks
- Part Three Information Theory and Psychology
- Part Four Formal Analysis
- 14 Quantitative Analysis of a Schaffer Collateral Model
- 15 A Quantitative Model of Information Processing in CA1
- 16 Stochastic Resonance and Bursting in a Binary-Threshold Neuron with Intrinsic Noise
- 17 Information Density and Cortical Magnification Factors
- Bibliography
- Index
Summary
Introduction
Stochastic resonance (SR) is a phenomenon whereby random fluctuations and noise can enhance the detectability and/or the coherence of a weak signal in certain nonlinear dynamical systems (see e.g. Moss et al. (1994a), Wiesenfeld and Moss (1995); Bulsara and Gammaitoni (1996) and references therein). There is growing evidence that SR may play a role in the extreme sensitivity exhibited by various sensory neurons (Longtin et al., 1991; Douglass et al., 1993; Bezrukov and Vodyanoy, 1995; Collins et al., 1996) it has also been suggested that SR could feature at higher levels of brain function, such as in the perceptual interpretation of ambiguous figures (Riani and Simonotto, 1994; Simonotto et al., 1997; Bressloff and Roper, 1998). In the language of information theory, the main topic of this volume, SR is a method for optimising the Shannon information transfer rate (transinformation) of a memoryless channel (Heneghan et al., 1996).
Most studies of SR have been concerned with external noise, that is, a stochastic forcing term is deliberately added to a non-linear system that is controllable by the experimentalist. The archetype is one of a damped particle moving in a double well potential. If the particle is driven by a weak periodic force, i.e. one in which the forcing amplitude is less than the barrier height, it will be confined to a single well and will oscillate about the minimum. However, if the particle is driven by weak noise it will switch between wells with a transition rate which depends exponentially on the noise strength, D (imagine cooking popcorn on a low heat in a large pan).
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- Information Theory and the Brain , pp. 290 - 304Publisher: Cambridge University PressPrint publication year: 2000