The Dynamic Brain An Exploration of Neuronal Variability and Its Functional Significance

by Ding, PhD, Mingzhou; Glanzman,PhD, Dennis

• ISBN13: 9780195393798
• ISBN10: 0195393791
• Edition: 1st
• Format: Hardcover
• Copyright: 2011-01-18
• Publisher: Oxford University Press

Summary

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 or stimulus 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 variability formerly 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 an underlying 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 various interdependence measures, including cross correlation, phase synchronization and spectral coherence. This book examines neuronal variability from theoretical, experimental and clinical perspectives.

Author Biography

Dennis Glanzman is Chief of the Theoretical and Computational Neuroscience Research Program at the National Institute of Mental Health, where he oversees a portfolio focused on the development and application of realistic models for the analysis and understanding of brain function. He was co-organizer of the joint NIH-NSF "Collaborative Research in Computational Neuroscience" Program which supports innovative interdisciplinary collaborative research to make significant advances in the understanding of nervous system function, mechanisms underlying nervous system disorders, and computational strategies used by the nervous system.

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.

Table of Contents

Contributors	p. xv
Characterizing Neuronal Variability
A Mixed-Filter Algorithm for Dynamically Tracking Learning from Multiple Behavioral and Neurophysiological Measures	p. 3
Stochastic Transitions between States of Neural Activity	p. 29
Neural Coding: Variability and Information	p. 47
Dynamics of Neuronal Ensembles
Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks	p. 65
Inherent Biases in Spontaneous Cortical Dynamics	p. 83
Phase Resetting in the Presence of Noise and Heterogeneity	p. 104
Understanding Animal-to-Animal Variability in Neuronal and Network Properties	p. 119
Dynamical Parameter and State Estimation in Neuron Models	p. 139
Neuronal Variability and Cognition
Capturing ôTrial-to-Trialö Variations in Human Brain Activity: From Laboratory to Real World	p. 183
Linking Neuronal Variability to Perceptual Decision Making via Neuroimaging	p. 214
Spatiotemporal Dynamics of Synchronous Activity across Multiple Areas of the Visual Cortex in the Alert Monkey	p. 233
Behavioral and Neural Variability Related to Stochastic Choices during a Mixed-Strategy Game	p. 255
Neuronal Variability and Brain Disorders
Circuit Mechanisms Underlying Behavioral Variability during Recovery of Consciousness following Severe Brain Injury	p. 279
Intermittent Vorticity, Power Spectral Scaling, and Dynamical Measures on Resting Brain Magnetic Field Fluctuations: A Pilot Study	p. 296
Population Variability and Bayesian Inference	p. 338
Index	p. 367
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