This short book will discuss the crucial role that statistical modelling plays in unravelling patterns arising from recording neuronal cell activities (spikes). The history of modern neuroscience coincides with the rise and popularity of statistical thought process - dating back to the beginning of the 20th century. As such, and from early on, spike trains (incidence of spiking activities, mapped out on a time scale), have been viewed as realizations of point processes. Consequently, the advancement of new means of recording spike trains, such as simultaneous recordings of multiple cells, has radically increased the demand for appropriate statistical analysis, whether through developing theoretical bases for multivariate stochastics processes, or via expanding the tools for capturing the networking of adjacent neurons.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Maße
Höhe: 216 mm
Breite: 138 mm
ISBN-13
978-1-138-19718-3 (9781138197183)
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Schweitzer Klassifikation
Autor*in
Dept of Mathematics, California State University, USA
University of California, Irvine, USA
Introduction and Overview. History. On History of Modern Statistics and Neuroscience. Neurophysiology and Brain Signals. Brain Anatomy, Neural Processes, Neuronal Recording, and Cognitive Neuroscience. Basic Neurophysiology (Experiments, Neuronal Activity). Brain Signals (Spike Trains, LFP, EEG - Explain These Signals, Removing Artefacts). Examples on Rats Data. Statistical Methods for Spike Trains. Sources of Uncertainty in Spike Train Recordings. Identifying Bursts in Neural Intensity Rates in Multiple Trials. Point Processes as a Tool for Modelling Spike Trains. Statistical Tools for Understanding Patterns in Simultaneously Recorded Neurons: Correlation and Synchrony. Examples on Rats Data. Statistical Methods for LFPs and EEGs. Basic Time Series (Meaning of the Spectrum and Coherence; Auto and Cross-correlation; Simple Time Series Models). Spectral and Coherence Analysis of EEGs and LFPs. Examples on Rats Data. Behavior and Neurophysiology. Basic Statistical Learning Methods. The Idea Behing Mixed Effects Models. Statistical Models for Neurophysiology and Behaviour. Role of Computation in Understanding the Intricacies of Neural Networks. Future, Some Predictions, and the Roadmap Ahead of Us.
