Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina - Jouty, Jonathan; Hilgen, Gerrit; Sernagor, Evelyne; Hennig, Matthias H. (Frontiers in Cellular Neuroscience, 2018)
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 automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate this approach with synthetic spike trains, and demonstrate that the major known cell types are discovered in a high-density recording from a mouse retina. We find that the major known cell types are identified from a single high-density recording session from the mouse retina with about 1000 retinal ganglion cells. As a parameter-free method, it is broadly applicable for the physiological classification of sensory neurons.