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  What the Mouse Eye Tells the Mouse Brain: A Semi-Supervised Clustering Approach for Fingerprinting the Retinal Ganglion Cell Types of the Mouse Retina

Berens, P., Baden, T., Franke, K., Rezac, M., Bethge, M., & Euler, T. (2014). What the Mouse Eye Tells the Mouse Brain: A Semi-Supervised Clustering Approach for Fingerprinting the Retinal Ganglion Cell Types of the Mouse Retina. Poster presented at AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece.

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http://areadne.org/2014/home.html (Abstract)
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 Creators:
Berens, P, Author              
Baden, T, Author
Franke, K, Author
Rezac, M, Author
Bethge, M1, 2, Author              
Euler, T, Author
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

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 Abstract: In the retina, the stream of incoming visual information is split into multiple parallel channels, formed by different kinds of photoreceptors (PRs), bipolar cells (BCs) and ganglion cells (RGCs). These cells form complex circuits with additional interneurons tuning the channels to distinct sets of visual features. The RGCs relay the output of each channel to the brain—understanding how the visual scenery is encoded by the outputs of the approximately 20 RGC types will thus yield a complete picture of the representation of the visual scene available to the brain. To identify a functional fingerprint for each RGC type in the mouse retina, we use 2P imaging to measure Ca++ activity in RGCs evoked by a set of stimuli, including frequency/contrast modulated full-field and white noise stimuli. So far our database contains recordings of over 10,000 cells from the RGC layer. In addition, we obtained recordings from transgenic PV1 mice, in which 8 morphologically distinct RGC types are fluorescently labeled and can be identified based on their anatomy. Moreover, we performed single-cell recordings from a few dozen RGCs to relate their spiking responses to the somatic calcium signals and to compare their morphologies with published RGC catalogues. We implemented a probabilistic clustering framework for separating RGCs into functional types based on features extracted from their responses to the different visual stimuli using PCA. We used a semi-supervised mixture of Gaussians Clustering algorithm, which allowed us to incorporate the uncertain label information provided by the recordings from the PV1 mice into the clustering. For our data, we obtain 25–29 functional clusters, which separate into 17–21 RGC clusters and 8 displaced amacrine cell (dAC) clusters, as verified using glutamatedecarboxylase (GAD) immunostaining. These numbers match well the number of RGC and dAC types expected in mouse retina. The RGC types include many known cell types (off and on alpha, W3, on-off direction-selective), as verified using our 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 RGC types, such as a contrast-suppressed type, not readily matched to previously described ones. Our results suggest that a functional fingerprint for each RGC in the mouse retina is within reach.

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 Dates: 2014-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: BerensBFRBE2014
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Title: AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles
Place of Event: Santorini, Greece
Start-/End Date: 2014-06-25 - 2014-06-29

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Title: AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles
Source Genre: Proceedings
 Creator(s):
Hatsopoulos, NG, Editor
Pezaris, JS, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : AREADNE Foundation
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 53 Identifier: ISSN: 2155-3203