Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina
Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-12-01
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| Series: | Frontiers in Cellular Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fncel.2018.00481/full |
| _version_ | 1828832649210232832 |
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| author | Jonathan Jouty Gerrit Hilgen Evelyne Sernagor Matthias H. Hennig |
| author_facet | Jonathan Jouty Gerrit Hilgen Evelyne Sernagor Matthias H. Hennig |
| author_sort | Jonathan Jouty |
| collection | DOAJ |
| description | Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1,000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities. |
| first_indexed | 2024-12-12T16:58:34Z |
| format | Article |
| id | doaj.art-129a5bf3a8ac4c8f97a628bd49618646 |
| institution | Directory Open Access Journal |
| issn | 1662-5102 |
| language | English |
| last_indexed | 2024-12-12T16:58:34Z |
| publishDate | 2018-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular Neuroscience |
| spelling | doaj.art-129a5bf3a8ac4c8f97a628bd496186462022-12-22T00:18:10ZengFrontiers Media S.A.Frontiers in Cellular Neuroscience1662-51022018-12-011210.3389/fncel.2018.00481422724Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse RetinaJonathan Jouty0Gerrit Hilgen1Evelyne Sernagor2Matthias H. Hennig3Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United KingdomInstitute of Neuroscience, Newcastle University, Newcastle, United KingdomInstitute of Neuroscience, Newcastle University, Newcastle, United KingdomInstitute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United KingdomRetinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1,000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities.https://www.frontiersin.org/article/10.3389/fncel.2018.00481/fullretinal ganglion cellsmulti-electrode arraylight responsesclassificationspike distance |
| spellingShingle | Jonathan Jouty Gerrit Hilgen Evelyne Sernagor Matthias H. Hennig Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina Frontiers in Cellular Neuroscience retinal ganglion cells multi-electrode array light responses classification spike distance |
| title | Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina |
| title_full | Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina |
| title_fullStr | Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina |
| title_full_unstemmed | Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina |
| title_short | Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina |
| title_sort | non parametric physiological classification of retinal ganglion cells in the mouse retina |
| topic | retinal ganglion cells multi-electrode array light responses classification spike distance |
| url | https://www.frontiersin.org/article/10.3389/fncel.2018.00481/full |
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