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Neural Information ProcessingFunctional Imaging Methods: Source Separation Techniques

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Functional Imaging Methods: Source Separation Techniques

Lupe

Optical recording techniques have become a widespread technique for measuring the activity of neural populations. Typically, the optical signal is generated by a mixture of sources, where only some of them are related to the optical signal which is correlated with the neural activity of interest. In addition, the signal-to-noise ratio is often very low, in particular, for single condition maps or and / short recording sequences. In this project we investigated whether recently blind source separation techniques (ICA, extended spatial decorrelation, generalized linear models, etc.) can be properly extended to cope with abovementioned challenges. For optical imaging of intrinsic signals we found, that second order methods based on spatial decorrelation algorithms provided the best results. Methods were applied to optical recording data using intrinsic signals as well as to calcium imaging data.

Acknowledgement: Research was funded by DFG, Wellcome Trust, and Technische Universität Berlin.

Selected Publications:

Regularized Second Order Source Separation
Citation key Schiessl2000c
Author Schießl, I. and Schöner, H. and Stetter, M. and Dima, A. and Obermayer, K.
Title of Book Int. Workshop on Independent Component Analysis and Blind Signal Separation
Pages 111 – 116
Year 2000
Volume 2
Editor P. Pajunen and J. Karhunen
Abstract In the separation task of linear mixtures from real experiments the dependencies of the original sources often make \"classical\" independent component analysis (ICA) algorithms fail. One way to overcome this drawback is the introduction of additional knowledge we have about the mixing process. We introduce a regularization term to the cost function of multishift extended spatial decorrelation (multishift ESD) that punishes the deviation of the time course of the estimated sources from a assumed time course during an experiment. In the case of optical imaging such knowledge can be achieved from the metabolic response of signals to the stimulus onset. We show how the regularization term improves the separation result at different noise levels. The simulations were run on a artificial toy dataset and one dataset that contains prototype signals from a real optical imaging experiment.
Bibtex Type of Publication Selected:sources
Link to publication Download Bibtex entry

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