ArticleFrontiers in cellular neuroscience2018
Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina.
Article in Frontiers in cellular neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A large-scale dataset of functional mouse ganglion cell layer responses.Scientific data · 2026Article
- Quantifying spike train synchrony and directionality: Measures and Applications.Biological cybernetics · 2026Review
- In vivo cell-type and brain region classification via multimodal contrastive learning.bioRxiv : the preprint server for biology · 2025Article
- Article
- Assessment of Murine Retinal Acuity Ex Vivo Using Multielectrode Array Recordings.Translational vision science & technology · 2023Article
- Spikebench: An open benchmark for spike train time-series classification.PLoS computational biology · 2023Article
- Longitudinal in vivo CaProceedings of the National Academy of Sciences of the United States of America · 2022Article
- Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression.Cell reports · 2022Article
- A novel approach to the functional classification of retinal ganglion cells.Open biology · 2022Article
- Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Spike-triggered Clustering for Retinal Ganglion Cell Classification.Experimental neurobiology · 2020Article
- Synchrony of Spontaneous Burst Firing between Retinal Ganglion Cells Across Species.Experimental neurobiology · 2020Article
- New Features of Receptive Fields in Mouse Retina through Spike-triggered Covariance.Experimental neurobiology · 2020Article
- Article
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4 authors.
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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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