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Functional Imaging Methods: Source Separation Techniques


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:

Blind Source Separation of Single Components from Linear Mixtures.
Citation key Vollgraf2001
Author Vollgraf, R. and Schießl, I. and Obermayer, K.
Title of Book Proc. Int. Conf. on Artificial Neural Networks - ICANN 01
Pages 509 – 514
Year 2001
ISBN 978-3-540-42486-4, 978-3-540-44668-2
ISSN 0302-9743
DOI 10.1007/3-540-44668-0_71
Publisher Springer Berlin Heidelberg
Abstract We present a new method, that is able to separate one or a few particular sources from a linear mixture, performing source separation and dimensionality reduction simultaneously. This is in particular useful in situations in which the number of observations is much larger than the number of underlaying sources, as it allows to drastically reduce the number of the parameters to estimate. It is well applicable for the long time series recorded in optical imaging experiments. Here one is basically interested in only one source containing the stimulus response. The algorithm is based on the technique of joint diagonalization of cross correlation matrices. To focus the convergence to the desired source, prior knowledge is incorporated. It can be derived, for instance, from the expected time course of the metabolic response in an optical imaging experiment. We demonstrate the capabilities of this algorithm on the basis of toy data coming from prototype signals of former optical recording experiments and with time courses that are similar to those obtained in optical recording experiments.
Bibtex Type of Publication Selected:sources
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