Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina

Frontiers in Cellular Neuroscience
Jonathan JoutyMatthias Helge Hennig

Abstract

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.

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Citations

Mar 4, 2020·Experimental Neurobiology·Jungryul AhnYong Sook Goo
Sep 15, 2020·Experimental Neurobiology·Jungryul AhnYong Sook Goo
Dec 16, 2020·Experimental Neurobiology·Jungryul AhnYong Sook Goo

Related Concepts

Brain
Classification
Laboratory mice
Neurons
Retina
Retinal Ganglion Cells
Evaluation
Paradigm
Study
Mouse Embryonic Stem Cells

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