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Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons

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Abstract

In spite of their anatomical robustness, it has been difficult to establish the functional role of corticogeniculate circuits connecting primary visual cortex with the lateral geniculate nucleus of the thalamus (LGN) in the feedback direction. Growing evidence suggests that corticogeniculate feedback does not directly shape the spatial receptive field properties of LGN neurons, but rather regulates the timing and precision of LGN responses and the information coding capacity of LGN neurons. We propose that corticogeniculate feedback specifically stabilizes the response gain of LGN neurons, thereby increasing their information coding capacity. Inspired by early work by McClurkin et al. (1994), we manipulated the activity of corticogeniculate neurons to test this hypothesis. We used optogenetic methods to selectively and reversibly enhance the activity of corticogeniculate neurons in anesthetized ferrets while recording responses of LGN neurons to drifting gratings and white noise stimuli. We found that optogenetic activation of corticogeniculate feedback systematically reduced LGN gain variability and increased information coding capacity among LGN neurons. Optogenetic activation of corticogeniculate neurons generated similar increases in information encoded in LGN responses to drifting gratings and white noise stimuli. Together, these findings suggest that the influence of corticogeniculate feedback on LGN response precision and information coding capacity could be mediated through reductions in gain variability.

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Acknowledgements

We thank Brianna Carr, Marc Mancarella, and Elise Bragg for expert technical assistance and Drs. Dana LeMoine, Wendy Bates, Diane Moorman-White, Jeff Wyatt, Karen Moodie, and Kirk Maurer for veterinary assistance. We thank Dr. Daniel Rathbun, Uday Chockanathan, and Dr. Krishnan Padmanabhan for helpful discussions of data analysis methods and comments on this manuscript.

Funding

This work was funded by the National Institutes of Health (National Eye Institute: EY018683 and EY025219 to F.B. and T32EY007125 to A.J.M.) and the Whitehall Foundation (2013-05-06). J.M.H. was supported by a Graduate Fellowship from the Albert J. Ryan Foundation.

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Authors and Affiliations

  1. Neuroscience Graduate Program, University of Rochester, Rochester, NY, 14642, USA

    Allison J. Murphy, Luke Shaw & Farran Briggs

  2. Center for Visual Science, University of Rochester, Rochester, NY, 14642, USA

    Allison J. Murphy & Farran Briggs

  3. Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine, 601 Elmwood Ave., Box 603, Rochester, NY, 14642, USA

    J. Michael Hasse & Farran Briggs

  4. Center for Neural Science, New York University, New York, NY, 10003, USA

    J. Michael Hasse

  5. Institute for Neuroscience, University of Texas at Austin, Austin, TX, 78712, USA

    Robbe L. T. Goris

  6. Department of Psychology, University of Texas at Austin, Austin, TX, 78712, USA

    Robbe L. T. Goris

  7. Department of Neuroscience, University of Rochester School of Medicine, Rochester, NY, 14642, USA

    Farran Briggs

  8. Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, 14642, USA

    Farran Briggs

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  1. Allison J. Murphy
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Contributions

A.J.M, R.L.T.G., and F.B. designed the experiments. A.J.M. and J.M.H. collected the data. A.J.M., L.S., and F.B. analyzed the data. A.J.M., R.L.T.G., and F.B. wrote the manuscript.

Corresponding author

Correspondence to Farran Briggs.

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All of the authors declare no financial conflicts of interest or competing interests related to this study.

Availability of data and material

Data generated for this study will be made available upon reasonable written request to the corresponding author.

Code availability

Variance code is available here: https://gorislab.github.io/resources/ Information theory analysis code is available here: https://github.com/BriggsNeuro/InfoVarianceCode​.

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Action Editor: Aasef G. Shaikh

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This article belongs to the Topical Collection: Vision and Action Guest Editors: Aasef Shaikh and Jeffrey Shall.

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Murphy, A.J., Shaw, L., Hasse, J.M. et al. Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons. J Comput Neurosci 49, 259–271 (2021). https://doi.org/10.1007/s10827-020-00754-5

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