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Going to temporal superresolution for AP detection in two photon calcium imaging in vivo by using an explicit datamodel

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons85589

Kosten,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83945

Greenberg,  D
Research Group Neural Population Imaging, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84010

Kerr,  J
Research Group Neural Population Imaging, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kosten, J., Greenberg, D., Bethge, M., & Kerr, J. (2008). Going to temporal superresolution for AP detection in two photon calcium imaging in vivo by using an explicit datamodel. Poster presented at 9th Conference of the Junior Neuroscientists of Tübingen (NeNa 2008), Ellwangen, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C6D7-6
Abstract
Two photon calcium imaging in vivo allows for the simultaneous imaging of activity in populations of cortical neurons. This approach has been shown to achieve both single action potential (AP) and single cell resolution, an important requirement when measuring neural activity. However, there still remains room for improvement in both data acquisition and data analysis. Imaging calcium transients across time allows the inference of electrical spiking activity, but since the calcium signals are an order of magnitude slower than the spiking activity which produces them, temporal accuracy can be lost. Here we describe a possible approach to increase the temporal resolution of such data. We present an approach that explicitly models signal and noise in the data, and complements the output of a previous spike detection algorithm. Instead of averaging the signal over 96 ms (a full frame), we employ higher resolution that averages over 1.5 ms periods, corresponding to the individual laser scan lines that compose a single image frame. The dierence between theoretical and observed uorescence measurements is modeled as a multivariate Gaussian distribution with zero mean, yielding a likelihood value for each possible spike time over a two frame window. Taking into account the prior distribution of timing errors in the output of our AP detection algorithm, we estimate the detected spike's most likely position. This approach improves temporal resolution signicantly compared to previous methods. We discuss the future development of this approach, its limitations, and the crucial role of an accurate estimation of baseline uorescence.