Reproducibility – a new approach to estimating significance of orientation and direction coding
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1
Ruhr University Bochum, Bernstein Group for Computational Neuroscience, Institut für Neuroinformatik, Germany
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2
Ruhr University Bochum, International Graduate School of Neuroscience, Germany
We propose a method estimating the reliability of orientation (or direction) coding, by examining the reproducibility of the preferred orientation (direction) measured across trials. A resulting normalized measure, with values between 0 and 1, is easily transformed to p-values, providing explicit statistical significance information of orientation (direction) coding of the recorded response. Selectivity to orientation of contours (or direction of their motion) has been a thoroughly studied feature of the visual system. A standard experimental procedure involves recording a sequence of responses to a clearly oriented pattern (for example – sinusoidal gratings), while varying the orientation angle. A number of methods to estimate a preferred orientation were proposed (Swindale, 1998), with each model function providing a different measure of orientation selectivity. Those more intuitive – like the width of a Gaussian fitted to the response curve require a fine sampling of the orientation (direction) domain. The frequently used OSI (Orientation Selectivity Index) strongly depends on the measurement type and the signal-to-noise ratio. In contrast, our approach is applicable to any kind of signal and does not require sophisticated fitting methods. We present results from both electrophysiology and Optical Imaging recordings.
Conference:
Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.
Presentation Type:
Poster Presentation
Topic:
Neural encoding and decoding
Citation:
Grabska-Barwinska
A,
Shien-Wei-Ng
B and
Jancke
D
(2009). Reproducibility – a new approach to estimating significance of orientation and direction coding.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.neuro.10.2009.14.096
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Received:
27 Aug 2009;
Published Online:
27 Aug 2009.
*
Correspondence:
Agnieszka Grabska-Barwinska, Ruhr University Bochum, Bernstein Group for Computational Neuroscience, Institut für Neuroinformatik, Bochum, Germany, agnieszka.grabska-barwinska@rub.de