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Non-parametric physiological classification of retinal ganglion cells in the mouse retina

Lookup NU author(s): Dr Gerrit HilgenORCiD, Professor Evelyne SernagorORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018 Jouty, Hilgen, Sernagor and Hennig. 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.

Publication metadata

Author(s): Jouty J, Hilgen G, Sernagor E, Hennig MH

Publication type: Article

Publication status: Published

Journal: Frontiers in Cellular Neuroscience

Year: 2018

Volume: 12

Online publication date: 07/12/2018

Acceptance date: 26/11/2018

Date deposited: 10/01/2019

ISSN (electronic): 1662-5102

Publisher: Frontiers Research Foundation


DOI: 10.3389/fncel.2018.00481


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Funder referenceFunder name
RPG-2016-315Leverhulme Trust, The