September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
What the mouse eye tells the mouse's brain: a functional classification of retinal ganglion cells
Author Affiliations & Notes
  • Tom Baden
    Bernstein Centre for Computational Neuroscience, University of Tuebingen, Tuebingen, BW, Germany
    Centre for Integrative Neuroscience, University of Tuebingen, Tuebingen, BW, Germany
  • Footnotes
    Commercial Relationships   Tom Baden, None
  • Footnotes
    Support  This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Werner Reichardt Centre for Integrative Neuroscience Tübingen, EXC 307 to M.B. and T.E.; BA 5283/1-1 to T.B.; BE 5601/1-1 to P.B.), the German Federal Ministry of Education and Research (BMBF) (BCCN Tübingen, FKZ 01GQ1002 to M.B. and T.E.), the BW-Stiftung (AZ 1.16101.09 to T.B.) and the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health (U01NS090562 to T.E.).
Investigative Ophthalmology & Visual Science September 2016, Vol.57, No Pagination Specified. doi:
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      Tom Baden; What the mouse eye tells the mouse's brain: a functional classification of retinal ganglion cells. Invest. Ophthalmol. Vis. Sci. 201657(12):.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Presentation Description : In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells (RGCs). Each type encodes distinct visual features in parallel for transmission to the brain. Understanding how the visual scenery is encoded by the outputs of the different RGC types will yield a complete picture of the representation of the visual scene available to the brain. Here we present a functional characterization of the retinal output channels. We show that the number of RGC types is much higher than previously thought, including many novel types of RGC. To record from every cell in the ganglion cell layer we used bulk-electroporation (Briggman & Euler, 2011) and two-photon Ca2+ imaging. A standardized stimulus set, including temporal full-field stimulation, local motion, and dense noise for receptive field mapping, was presented to the retina. Also, electrical single-cell recordings were performed to relate RGC spiking to somatic Ca2+ signals, to retrieve RGC morphologies and to characterize single cell types in more detail. We implemented a probabilistic clustering framework for separating our sample of >11,000 cells (50 retinas) into functional clusters solely based on features extracted from their light responses using sparse PCA and mixture of Gaussians clustering. Then, the 70+ functional clusters were post-processed into “RGC groups” based on meta data, such as immunolabels and soma size. We found that RGCs can be divided into at least 32 functional types. These include many known cell types (OFF and ON alpha, W3, ON-OFF direction-selective), as verified using genetic label and single cell data (e.g. alpha RGCs) and additional information available (e.g. soma size/shape and retinal tiling). In addition, they include new functional types. For example, we identified a small asymmetric cell responding only to local stimuli and an ON transient DS RGC with a single cardinal direction. Also, we found a contrast-suppressed type and a colour-opponent RGC that have not been identified in mouse before. Taken together, our data indicates that information channels from the eye to the brain may be much more diverse than previously thought.
Authors: T Baden*, P Berens*, K Franke*, M Roman Roson, M Bethge, T Euler.
*equal contribution

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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