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Classification of pseudocalcium visual responses from mouse retinal ganglion cells

Published online by Cambridge University Press:  10 November 2021

H. Shabani
Affiliation:
Institute for Ophthalmic Research, Centre for Ophthalmology, Eberhard Karls University, Tübingen, Germany
Mahdi Sadeghi
Affiliation:
Institute for Ophthalmic Research, Centre for Ophthalmology, Eberhard Karls University, Tübingen, Germany
E. Zrenner
Affiliation:
Institute for Ophthalmic Research, Centre for Ophthalmology, Eberhard Karls University, Tübingen, Germany Werner Reichardt Centre for Integrative Neuroscience (CIN), Tübingen, Germany
D.L. Rathbun*
Affiliation:
Institute for Ophthalmic Research, Centre for Ophthalmology, Eberhard Karls University, Tübingen, Germany Department of Ophthalmology, Detroit Institute of Ophthalmology, Henry Ford Health System, Detroit, Michigan
Z. Hosseinzadeh*
Affiliation:
Department of Molecular and Cellular Mechanisms of Neurodegeneration, Paul Flechsig Institute for Brain Research, University of Leipzig, Leipzig, Germany
*
Corresponding author: D.L. Rathbun,e-mail: drathbu2@hfhs.org; Z. Hosseinzadeh, e-mail: Zohreh.Hosseinzadeh@medizin.uni-leipzig.de
Corresponding author: D.L. Rathbun,e-mail: drathbu2@hfhs.org; Z. Hosseinzadeh, e-mail: Zohreh.Hosseinzadeh@medizin.uni-leipzig.de

Abstract

Recently, a detailed catalog of 32 retinal ganglion cell (RGC) visual response patterns in mouse has emerged. However, the 10,000 samples required for this catalog—based on fluorescent signals from a calcium indicator dye—are much harder to acquire from the extracellular spike train recordings underlying our bionic vision research. Therefore, we sought to convert spike trains into pseudocalcium signals so that our data could be directly matched to the 32 predefined, calcium signal-based groups. A microelectrode array (MEA) was used to record spike trains from mouse RGCs of 29 retinas. Visual stimuli were adapted from the Baden et al. study; including moving bars, full-field contrast and temporal frequency chirps, and black–white and UV-green color flashes. Spike train histograms were converted into pseudocalcium traces with an OGB-1 convolution kernel. Response features were extracted using sparse principal components analysis to match each RGC to one of the 32 RGC groups. These responses mapped onto of the 32 previously described groups; however, some of the groups remained unmatched. Thus, adaptation of the Baden et al. methodology for MEA recordings of spike trains instead of calcium recordings was partially successful. Different classification methods, however, will be needed to define clear RGC groups from MEA data for our bionic vision research. Nevertheless, others may pursue a pseudocalcium approach to reconcile spike trains with calcium signals. This work will help to guide them on the limitations and potential pitfalls of such an approach.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

This is an updated version of the original article. For details please see the notice at https://doi.org/10.1017/S0952523822000037.

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