June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Toward a complete functional classification of ganglion cells in the rat retina
Author Affiliations & Notes
  • Sneha Ravi
    Biomedical Engineering, University of Southern California, Los Angeles, CA
  • Daniel Ahn
    Systems Neurobiology Laboratory, The Salk Institute, La Jolla, CA
  • Martin Greschner
    Systems Neurobiology Laboratory, The Salk Institute, La Jolla, CA
  • Alan Litke
    Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA
  • E. Chichilnisky
    Systems Neurobiology Laboratory, The Salk Institute, La Jolla, CA
  • Greg Field
    Cell and Neurobiology, University of Southern California, Los Angeles, CA
  • Footnotes
    Commercial Relationships Sneha Ravi, None; Daniel Ahn, None; Martin Greschner, None; Alan Litke, None; E. Chichilnisky, None; Greg Field, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3387. doi:
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      Sneha Ravi, Daniel Ahn, Martin Greschner, Alan Litke, E. Chichilnisky, Greg Field; Toward a complete functional classification of ganglion cells in the rat retina. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3387.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: It is unclear how many distinct retinal ganglion cell (RGC) types send information from the retina to the brain. Morphological classifications of mammalian RGCs have identified ~20 distinct types while physiological classifications have typically identified only 8-12 types. However, physiological studies have recorded from only one or a few neurons at a time and/or have used a limited set of visual stimuli. The purpose of this study is to determine how many functionally distinct RGC types are present in the rat retina by using diverse visual stimuli and by recording from hundreds of RGCs simultaneously.

Methods: Action potentials from RGCs in the rat retina were recorded in an ex vivo isolated retina preparation using a 512-electrode array. The image from a calibrated visual display was focused on the photoreceptors and several different visual stimuli were used to drive RGC responses: spatiotemporal white noise, drifting sinusoidal gratings, and full-field light increments and decrements. Recorded spikes were sorted offline to identify distinct RGCs. RGCs were divided into functional classes based on their response properties to visual stimuli and their intrinsic spiking properties. Several different techniques for combining data obtained from different stimulus presentations were tested to achieve the most effective classification.

Results: Individual recording sessions identified the spikes from 400-500 RGCs over the MEA. A single visual stimulus, spatiotemporal white noise, allowed for a limited classification of RGCs into just a handful of subtypes. However, combining the response properties of cells across several visual stimuli greatly increased the number of identified RGC types. The receptive fields of each type produced a regular tiling pattern or “mosaic”, suggesting that RGC types sample space in this manner and that each identified RGC type was not further divisible. This resulted in a functional classification that nearly matched the morphological RGC diversity.

Conclusions: These results suggest that the functional diversity among RGCs is well predicted by the observed morphological diversity. Furthermore, they support the idea that RGCs in the mammalian retina sample space as a mosaic. Finally, this study highlights the need for using diverse visual stimuli when classifying RGCs and the utility of large-scale parallel recordings of neural activity.

Keywords: 531 ganglion cells • 673 receptive fields • 693 retinal connections, networks, circuitry  
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