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

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.

Table of Contents:
TABLE OF CONTENTS Contributors PART 1: Characterizing Neuronal Variability Chapter 1: A Mixed-filter Algorithm for Dynamically Tracking Learning from Multiple Behavioral and Neurophysiological Measures Todd P. Coleman, Marianna Yanike, Wendy Suzuki, and Emery N. Brown Chapter 2: Stochastic Transitions between States of Neural Activity Paul Miller and Donald B. Katz Chapter 3: Neural Coding: Variability and Information Richard B. Stein and Dirk G. Everaert PART 2: Dynamics of Neuronal Ensembles Chapter 4: Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks Larry F. Abbott, Kanaka Rajan, and Haim Sompolinsky Chapter 5: Inherent Biases in Spontaneous Cortical Dynamics Chou P. Hung, Benjamin M. Ramsden, and Anna Wang Roe Chapter 6: Phase Resetting in the Presence of Noise and Heterogeneity Srisairam Achuthan, Fred H. Sieling, Astrid A. Prinz, and Carmen C. Canavier Chapter 7: Understanding Animal-to-Animal Variability in Neuronal and Network Properties Astrid A. Prinz, Tomasz G. Smolinski, and Amber E. Hudson Chapter 8: Dynamical Parameter and State Estimation in Neuron Models Henry D.I. Abarbanel, Paul Bryant, Philip E. Gill, Mark Kostuk, Justin Rofe, Zakary Singer, Bryan Toth, and Elizabeth Wong PART 3: Neuronal Variability and Cognition Chapter 9: Capturing "Trial-to-Trial" Variations in Human Brain Activity: from Laboratory to Real World Akaysha C. Tang, Matthew T. Sutherland, and Zhen Yang Chapter 10: Linking Neuronal Variability to Perceptual Decision Making via Neuroimaging Paul Sajda, Marios G. Philiastides, Hauke Heekeren, and Roger Ratcliff Chapter 11: Spatiotemporal Dynamics of Synchronous Activity Across Multiple Areas of the Visual Cortex in the Alert Monkey Charles M. Gray and Baldwin Goodell Chapter 12: Behavioral and Neural Variability related to Stochastic Choices during a Mixed-Strategy Game Daeyeol Lee and Hyojung Seo PART 4: Neuronal Variability and Brain Disorders Chapter 13: Circuit Mechanisms Underlying Behavioral Variability after Severe Brain Injury Nicholas D. Schiff Chapter 14: Intermittent Vorticity, Power Spectral Scaling and Dynamical Measures on Resting Brain Magnetic Field Fluctuations Arnold J. Mandell, Karen A. Selz, Tom Holroyd, Lindsay Rutter, and Richard Coppola Chapter 15: Population Variability and Bayesian Inference Terran Lane Index

About the Author :
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 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.

Review :
"The editors have done an excellent job of assembling scientists from varied disciplines and different model systems...This book will appeal to physicians and scientists curious about the ideas and methods that underlie these exciting new developments in neuroscience. This is not one of the innumerable popular science books that waxes philosophical about how the brain may work; rather, it presents current, high-level ideas about how the brain processes signals and descriptions of the cutting-edge methods being used to examine this question." -- JAMA