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Analysis of Neural Data

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Blind Separation of Spatial Signal Patterns from Optical Imaging Records
Citation key Schiessl1999
Author Schiessl, I. and Stetter, M. and Mayhew, J. and Askew, S., and McLoughlin, N. and Levitt, J. and Lund, J. and Obermayer, K.
Title of Book ICA99 - International workshop on Idependent Component Analysis and Blind Source Separation
Pages 179 – 184
Year 1999
Abstract Optical imaging of intrinsic signals measures two-dimensional neuronal activity patterns by detecting small activity-related changes in the light reflectance of neural tissue. We test, to what extent blind source separation methods, which are based on the spatial independence of different signal components, are suitable for the separation of these neural-activity related signal components from nonspecific background variations of the light reflectance. Two ICA algorithms (Infomax and kurtosis optimization) and blind source separation by extended spatial decorrelation are compared to each other with respect to their robustness against sensor noise, and are applied to optical recordings from macaque primary visual cortex. We find that extended spatial decorrelation is superior to both the ICA algorithms and standart methods, because it explicitely takes advantage of the spatial smoothness of the intrinsic signal components.
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