The Dynamic Brain: An Exploration of Neuronal Variability and its Functional Significance

Hardcover | January 15, 2011

EditorMingzhou Ding, Dennis L. Glanzman

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It is a well-known fact of neurophysiology that neuronal responses to identically presented stimuli are extremely variable. This variability has in the past often been regarded as "noise." At the single neuron level, interspike interval (ISI) histograms constructed during either spontaneous orstimulus evoked activity reveal a Poisson type distribution. These observations have been taken as evidence that neurons are intrinsically "noisy" in their firing properties. In fact, the use of averaging techniques, like post-stimulus time histograms (PSTH) or event-related potentials (ERPs)have largely been justified based on the presence of what was believed to be noise in the neuronal responses.More recent attempts to measure the information content of single neuron spike trains have revealed that a surprising amount of information can be coded in spike trains even in the presence of trial-to-trial variability. Multiple single unit recording experiments have suggested that variabilityformerly attributed to noise in single cell recordings may instead simply reflect system-wide changes in cellular response properties. These observations raise the possibility that, at least at the level of neuronal coding, the variability seen in single neuron responses may not simply reflect anunderlying noisy process. They further raise the very distinct possibility that noise may in fact contain real, meaningful information which is available for the nervous system in information processing.To understand how neurons work in concert to bring about coherent behavior and its breakdown in disease, neuroscientists now routinely record simultaneously from hundreds of different neurons and from different brain areas, and then attempt to evaluate the network activities by computing variousinterdependence measures, including cross correlation, phase synchronization and spectral coherence. This book examines neuronal variability from theoretical, experimental and clinical perspectives.

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It is a well-known fact of neurophysiology that neuronal responses to identically presented stimuli are extremely variable. This variability has in the past often been regarded as "noise." At the single neuron level, interspike interval (ISI) histograms constructed during either spontaneous orstimulus evoked activity reveal a Poisson t...

Mingzhou Ding is J. Crayton Pruitt Family Professor in the Department of Biomedical Engineering at the University of Florida. His past work dealt with nonlinear dynamical systems and stochastic processes. Currently, he is interested in cognitive neuroscience and related computational and signal processing problems. Dennis Glanzman is ...
Format:HardcoverDimensions:400 pages, 9.25 × 6.12 × 0.98 inPublished:January 15, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0195393791

ISBN - 13:9780195393798

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Table of Contents

ContributorsPART 1: Characterizing Neuronal Variability1. Todd P. Coleman, Marianna Yanike, Wendy Suzuki, and Emery N. Brown: A Mixed-filter Algorithm for Dynamically Tracking Learning from Multiple Behavioral and Neurophysiological Measures2. Paul Miller and Donald B. Katz: Stochastic Transitions between States of Neural Activity3. Richard B. Stein and Dirk G. Everaert: Neural Coding: Variability and InformationPART 2: Dynamics of Neuronal Ensembles4. Larry F. Abbott, Kanaka Rajan, and Haim Sompolinsky: Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks5. Chou P. Hung, Benjamin M. Ramsden, and Anna Wang Roe: Inherent Biases in Spontaneous Cortical Dynamics6. Srisairam Achuthan, Fred H. Sieling, Astrid A. Prinz, and Carmen C. Canavier: Phase Resetting in the Presence of Noise and Heterogeneity7. Astrid A. Prinz, Tomasz G. Smolinski, and Amber E. Hudson: Understanding Animal-to-Animal Variability in Neuronal and Network Properties8. Henry D.I. Abarbanel, Paul Bryant, Philip E. Gill, Mark Kostuk, Justin Rofe, Zakary Singer, Bryan Toth, and Elizabeth Wong: Dynamical Parameter and State Estimation in Neuron ModelsPART 3: Neuronal Variability and Cognition9. Akaysha C. Tang, Matthew T. Sutherland, and Zhen Yang: Capturing "Trial-to-Trial" Variations in Human Brain Activity: from Laboratory to Real World10. Paul Sajda, Marios G. Philiastides, Hauke Heekeren, and Roger Ratcliff: Linking Neuronal Variability to Perceptual Decision Making via Neuroimaging11. Charles M. Gray and Baldwin Goodell: Spatiotemporal Dynamics of Synchronous Activity Across Multiple Areas of the Visual Cortex in the Alert Monkey12. Daeyeol Lee and Hyojung Seo: Behavioral and Neural Variability related to Stochastic Choices during a Mixed-Strategy GamePART 4: Neuronal Variability and Brain Disorders13. Nicholas D. Schiff: Circuit Mechanisms Underlying Behavioral Variability after Severe Brain Injury14. Arnold J. Mandell, Karen A. Selz, Tom Holroyd, Lindsay Rutter, and Richard Coppola: Intermittent Vorticity, Power Spectral Scaling and Dynamical Measures on Resting Brain Magnetic Field Fluctuations15. Terran Lane: Population Variability and Bayesian InferenceIndex