July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Cones adapt to higher-order stimulus statistics.
Author Affiliations & Notes
  • Matthew Yedutenko
    Retinal Signal Processing Lab, Netherlands Institute for Neuroscience, Diemen, Netherlands
  • Marcus Howlett
    Retinal Signal Processing Lab, Netherlands Institute for Neuroscience, Diemen, Netherlands
  • Maarten Kamermans
    Retinal Signal Processing Lab, Netherlands Institute for Neuroscience, Diemen, Netherlands
    Department of Genom Analysis, Academic Medical Center, University of Amsterdan, Amsterdam, Netherlands
  • Footnotes
    Commercial Relationships   Matthew Yedutenko, None; Marcus Howlett, None; Maarten Kamermans, None
  • Footnotes
    Support  Horizon 2020 Marie Sklodowska-Curie grant agreement No 674901 switchBoard
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 1977. doi:
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      Matthew Yedutenko, Marcus Howlett, Maarten Kamermans; Cones adapt to higher-order stimulus statistics.. Invest. Ophthalmol. Vis. Sci. 2018;59(9):1977.

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

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Purpose : Efficient representation of the visual stimuli requires Gaussian distribution of the cone output. However, the distribution of the intensities in the natural environment is skewed. Most natural scenes have positive skews: i.e. they are dominated by low intensities with sparse and brief episodes of high intensities. Scenes with artificial negative skews are dominated by high intensities with a small number of short episodes of low intensities. Here we investigated if and how cones redistribute such stimuli over their membrane potential and how this improves their performance.

Methods : Goldfish cones were stimulated with two sets of six monochromatic stimuli, each 2s long. The first set (positively skewed stimuli) was obtained from Van Hateren natural stimulus collection (Van Der Schaaf et.al 1996). The other set (skewed negatively) was custom constructed. All stimuli had the same mean light intensity and contrast, but with skewness ranging from 2.49 to -2.49. Data was acquired using patch clamp recording techniques.

Results : We found that M-cones are able to redistribute both positive (natural) and negative (artificial) skewed stimuli while S-cones can only redistribute the positive skewed stimuli. By comparing voltage clamp and current clamp recordings, we conclude that the mechanism responsible for positive skews redistribution resides within the phototransduction cascade, while the mechanism for negative skew redistribution depends on a membrane processes as well. Consistent with this notion is that positive skewed redistribution is reasonably well predicted by the Van Hateren phototransduction model (Van Hateren 2005, Endeman et al., 2010) for both M- and S-cones while negative skews are not. Since negative skews redistribution depends on membrane processes and is absent in S-cones, the most likely source of this adaptation is the membrane current Ih since M-cones express Ih strongly while S-cones do not. Modulation of Ih affects the kinetics of cones (Howlett et. al 2017) and indeed we found that the -3dB cutoff frequency changes with different stimulus skewness.

Conclusions : Obtained results show that there are two different mechanism of the redistribution of the stimuli skewness: 1) a non-linearity in the phototransduction cascade and 2) a membrane process which is absent in S-cones, most likely Ih.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.


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