Stetter Exploration of Cortical Function

Imaging and Modeling Cortical Population Coding Strategies
Erscheinungsjahr 2012
ISBN: 978-94-010-0430-5
Verlag: Springer Netherland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 269 Seiten, eBook

Exploration of Cortical Function summarizes recent research efforts aiming at the revelation of cortical population coding and signal processing strategies. Topics include optical detection techniques of population activity in the sub-millimeter range, advanced methods for the statistical analysis of these data, and biologically inspired neuronal modeling techniques for population activities in the frameworks of optimal coding, statistical learning theory, and mean-field recurrent networks. Exploration of Cortical Function is unique in that it covers one complete branch of population-based brain research ranging from techniques for data acquisition over data analysis up to modeling techniques for the quantification of functional principles. The volume covers an area which is of great current interest to researchers working on cerebral cortex. The combination of models and image analysis techniques to examine the activity of large cohorts of neurons is especially intriguing and prone to considerable error and debate.

Zielgruppe

Research

Autoren/Hrsg.

Stetter, M.

Inhaltsverzeichnis

1. Introduction.- 2. Neurons and Neuronal Signal.- 1 Overview.- 2 The Resting Potential.- 3 Passive Signal Propagation in Dendrites.- 4 Active Propagation of Spikes.- 5 Synaptic Transmission and Plasticity.- 3 The Early Visual System of Macaque Monkeys.- 1 Anatomy of the Early Visual Pathway.- 2 Physiology of the Early Visual Pathway.- 4 Optical Imaging of Brain Activity.- 1 Principles of Optical Imaging.- 2 Monte-Carlo Simulation of Optical Imaging.- 3 Results for Simulated Video Imaging.- 4 Simulated Scanning Laser Optical Imaging.- 5 Optical Imaging as Source Separation Problem.- 1 Formulation of the Source Separation Task.- 2 Heuristic Methods for Source Separation.- 6 Regression Methods for Source Separation.- 1 General Linear Models.- 2 Nonlinear Parametric Models.- 7 Projection Methods for Source Separation.- 1 Principle.- 2 Principal Component Analysis.- 3 Independent Component Analysis.- 4 Second Order Decorrelation Methods.- 5 Examples and Benchmarks.- 8 Real World Applications of Source Separation Techniques.- 1 Nonlinear Model-Analysis of Calcium Imaging Signals.- 2 PCA for Optical Imaging of Intrinsic Signals.- 3 ESD for Optical Imaging Data.- 4 Concluding Remarks.- 9 Computational Models of Early Vision.- 1 Why Computational Models?.- 2 Statistics of Natural Images.- 3 Compact Coding.- 4 Factorial Codes.- 5 Sparse Distributed Coding.- 10 Mean-Field Modeling of Cortical Function.- 1 Iceberg-Model of Orientation Selectivity.- 2 Mean-Field Model of Neuronal Population Activity.- 3 Modeling Orientation Selectivity with two Cell Types.- 4 Modeling Contrast Saturation with two Cell Types.- 5 Hypercolumns with Multiple Populations.- 6 Mean-Field Modeling of Contextual Effects.- 7 Concluding Remarks.- References.