目次

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.