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Understanding the retinal basis of vision across species


The vertebrate retina first evolved some 500 million years ago in ancestral marine chordates. Since then, the eyes of different species have been tuned to best support their unique visuoecological lifestyles. Visual specializations in eye designs, large-scale inhomogeneities across the retinal surface and local circuit motifs mean that all species’ retinas are unique. Computational theories, such as the efficient coding hypothesis, have come a long way towards an explanation of the basic features of retinal organization and function; however, they cannot explain the full extent of retinal diversity within and across species. To build a truly general understanding of vertebrate vision and the retina’s computational purpose, it is therefore important to more quantitatively relate different species’ retinal functions to their specific natural environments and behavioural requirements. Ultimately, the goal of such efforts should be to build up to a more general theory of vision.

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Fig. 1: Retinal composition across species.
Fig. 2: Differential retinal ganglion cell topographies support vision in different visual environments.
Fig. 3: Specializations of retinal neurons across the retina.
Fig. 4: Theoretical accounts of retinal designs.


  1. 1.

    Land, M. F. & Nilsson, D.-E. Animal Eyes (Oxford Univ. Press, 2012).

    Google Scholar 

  2. 2.

    Cronin, T. W., Johnsen, S., Marshall, N. J. & Warrant, E. J. Visual Ecology (Princeton Univ. Press, 2014).

    Google Scholar 

  3. 3.

    Zimmermann, M. J. Y. et al. Zebrafish differentially process color across visual space to match natural scenes. Curr. Biol. 28, 2018–2032 (2018). A study on larval zebarfish showing how the function of inner retinal circuits varies considerably across the eye to meet natural demands.

    CAS  PubMed  Google Scholar 

  4. 4.

    Turner, M. H. & Rieke, F. Synaptic rectification controls nonlinear spatial integration of natural visual inputs. Neuron 90, 1257–1271 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kühn, N. K. & Gollisch, T. Activity correlations between direction-selective retinal ganglion cells synergistically enhance motion decoding from complex visual scenes. Neuron 101, 963–976 (2019).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Baden, T. et al. A tale of two retinal domains: near-optimal sampling of achromatic contrasts in natural scenes through asymmetric photoreceptor distribution. Neuron 80, 1206–1217 (2013).

    CAS  PubMed  Google Scholar 

  7. 7.

    Bleckert, A., Schwartz, G. W., Turner, M. H., Rieke, F. & Wong, R. O. L. Visual space is represented by nonmatching topographies of distinct mouse retinal ganglion cell types. Curr. Biol. 24, 310–315 (2014). A study on mice showing that several types of RGCs exhibit distinct properties depending on their position on the retina.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Warwick, R. A., Kaushansky, N., Sarid, N., Golan, A. & Rivlin-Etzion, M. Inhomogeneous encoding of the visual field in the mouse retina. Curr. Biol. 28, 655–665 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Sabbah, S. et al. A retinal code for motion along the gravitational and body axes. Nature 546, 492–497 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Yoshimatsu, T., Schröder, C., Berens, P. & Baden, T. Cellular and molecular mechanisms of photoreceptor tuning for prey capture in larval zebrafish. Preprint at bioRxiv (2019).

  11. 11.

    Szatko, K. P. et al. Neural circuits in the mouse retina support color vision in the upper visual field. Preprint at bioRxiv (2019).

  12. 12.

    Dehmelt, F. A. et al. Spherical arena reveals optokinetic response tuning to stimulus location, size and frequency across entire visual field of larval zebrafish. Preprint at bioRxiv (2019).

  13. 13.

    Heitman, A. et al. Testing pseudo-linear models of responses to natural scenes in primate retina. Preprint at bioRxiv (2016).

  14. 14.

    Shah, N. P. et al. Inference of nonlinear spatial subunits by spike-triggered clustering in primate retina. Preprint at bioRxiv (2018).

  15. 15.

    Attneave, F. Some informational aspects of visual perception. Psychol. Rev. 61, 183–193 (1954).

    CAS  PubMed  Google Scholar 

  16. 16.

    Barlow, H. B. in Sensory Communication Ch. 13 (ed. Rosenblith, W. A.) (MIT Press, 1961).

  17. 17.

    Wässle, H. Parallel processing in the mammalian retina. Nat. Rev. Neurosci. 5, 747–757 (2004).

    PubMed  Google Scholar 

  18. 18.

    Masland, R. H. The neuronal organization of the retina. Neuron 76, 266–280 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Chapot, C. A., Euler, T. & Schubert, T. How do horizontal cells ‘talk’ to cone photoreceptors? Different levels of complexity at the cone-horizontal cell synapse. J. Physiol. 595, 5495–5506 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Thoreson, W. B. & Mangel, S. C. Lateral interactions in the outer retina. Prog. Retin. Eye Res. 31, 407–441 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Euler, T. T., Haverkamp, S. S., Schubert, T. T. & Baden, T. Retinal bipolar cells: elementary building blocks of vision. Nat. Rev. Neurosci. 15, 507–519 (2014).

    CAS  PubMed  Google Scholar 

  22. 22.

    Masland, R. H. The tasks of amacrine cells. Vis. Neurosci. 29, 3–9 (2012).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Franke, K. & Baden, T. General features of inhibition in the inner retina. J. Physiol. 595, 5507–5515 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Baccus, S. A. Timing and computation in inner retinal circuitry. Annu. Rev. Physiol. 69, 271–290 (2007).

    CAS  PubMed  Google Scholar 

  25. 25.

    Diamond, J. S. Inhibitory interneurons in the retina: types, circuitry, and function. Annu. Rev. Vis. Sci. 3, 1–24 (2017).

    PubMed  Google Scholar 

  26. 26.

    Sanes, J. R. & Masland, R. H. The types of retinal ganglion cells: current status and implications for neuronal classification. Annu. Rev. Neurosci. 38, 221–246 (2014).

    Google Scholar 

  27. 27.

    Dhande, O. S. & Huberman, A. D. Retinal ganglion cell maps in the brain: implications for visual processing. Curr. Opin. Neurobiol. 24, 133–142 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Kuffler, S. W. Discharge patterns and functional organization of mammalian retina. J. Neurophysiol. 16, 37–68 (1953).

    CAS  PubMed  Google Scholar 

  29. 29.

    Protti, Da, Flores-Herr, N. & von Gersdorff, H. Light evokes Ca2+ spikes in the axon terminal of a retinal bipolar cell. Neuron 25, 215–227 (2000).

    CAS  PubMed  Google Scholar 

  30. 30.

    Baden, T., Esposti, F., Nikolaev, A. & Lagnado, L. Spikes in retinal bipolar cells phase-lock to visual stimuli with millisecond precision. Curr. Biol. 21, 1859–1869 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Baden, T., Berens, P., Bethge, M. & Euler, T. Spikes in mammalian bipolar cells support temporal layering of the inner retina. Curr. Biol. 23, 48–52 (2012).

    PubMed  Google Scholar 

  32. 32.

    Puthussery, T., Venkataramani, S., Gayet-Primo, J., Smith, R. G. & Taylor, W. R. NaV1.1 channels in axon initial segments of bipolar cells augment input to magnocellular visual pathways in the primate retina. J. Neurosci. 33, 16045–16059 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Saszik, S. & DeVries, S. H. A mammalian retinal bipolar cell uses both graded changes in membrane voltage and all-or-nothing Na+ spikes to encode light. J. Neurosci. 32, 297–307 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Franke, K. et al. Inhibition decorrelates visual feature representations in the inner retina. Nature 542, 439–444 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    James, B., Darnet, L., Moya-Díaz, J., Seibel, S.-H. & Lagnado, L. An amplitude code transmits information at a visual synapse. Nat. Neurosci. 22, 1140–1147 (2019).

    CAS  PubMed  Google Scholar 

  36. 36.

    Baden, T., Euler, T., Weckström, M. & Lagnado, L. Spikes and ribbon synapses in early vision. Trends Neurosci. 36, 480–488 (2013).

    CAS  PubMed  Google Scholar 

  37. 37.

    Baden, T., Schubert, T., Berens, P. & Euler, T. The functional organization of vertebrate retinal circuits for vision. Oxford Res. Encycl. Neurosci. (2018).

  38. 38.

    Bloomfield, S. A. & Dacheux, R. F. Rod vision: pathways and processing in the mammalian retina. Prog. Retin. Eye Res. 20, 351–384 (2001).

    CAS  PubMed  Google Scholar 

  39. 39.

    Mauss, A. S., Vlasits, A., Borst, A. & Feller, M. Visual circuits for direction selectivity. Annu. Rev. Neurosci. 40, 211–230 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wässle, H., Puller, C., Müller, F. & Haverkamp, S. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina. J. Neurosci. 29, 106–117 (2009).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Breuninger, T., Puller, C., Haverkamp, S. & Euler, T. Chromatic bipolar cell pathways in the mouse retina. J. Neurosci. 31, 6504–6517 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Sun, W., Li, N. & He, S. Large-scale morphological survey of mouse retinal ganglion cells. J. Comp. Neurol. 451, 115–126 (2002).

    PubMed  Google Scholar 

  43. 43.

    Völgyi, B., Chheda, S. & Bloomfield, S. A. Tracer coupling patterns of the ganglion cell subtypes in the mouse retina. J. Comp. Neurol. 512, 664–687 (2009).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Bae, J. A. et al. Digital museum of retinal ganglion cells with dense anatomy and physiology. Cell 173, 1293–1306 (2018). Serial section electron microscopy level anatomical classification of RGCs in the mouse.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Behrens, C. et al. Connectivity map of bipolar cells and photoreceptors in the mouse retina. eLife 5, 1206–1217 (2016).

    Google Scholar 

  46. 46.

    Kim, J. S. et al. Space–time wiring specificity supports direction selectivity in the retina. Nature 509, 331–336 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013).

    CAS  PubMed  Google Scholar 

  48. 48.

    Borst, A. & Euler, T. Seeing things in motion: models, circuits, and mechanisms. Neuron 71, 974–994 (2011).

    CAS  PubMed  Google Scholar 

  49. 49.

    Baden, T. et al. The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350 (2016). Large-scale functional account of RGCs in the mouse.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Nath, A. & Schwartz, G. W. Cardinal orientation selectivity is represented by two distinct ganglion cell types in mouse retina. J. Neurosci. 36, 3208–3221 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Venkataramani, S. & Taylor, W. R. Orientation selectivity in rabbit retinal ganglion cells is mediated by presynaptic inhibition. J. Neurosci. 30, 15664–15676 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Venkataramani, S. & Taylor, W. R. Synaptic mechanisms generating orientation selectivity in the ON pathway of the rabbit retina. J. Neurosci. 36, 3336–3349 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Nath, A. & Schwartz, G. W. Electrical synapses convey orientation selectivity in the mouse retina. Nat. Commun. 8, 2025 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Krieger, B., Qiao, M., Rousso, D. L., Sanes, J. R. & Meister, M. Four alpha ganglion cell types in mouse retina: Function, structure, and molecular signatures. PLOS ONE 12, e0180091 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Jacoby, J. & Schwartz, G. W. Three small-receptive-field ganglion cells in the mouse retina are distinctly tuned to size, speed, and object motion. J. Neurosci. 37, 610–625 (2017). A study on mice describing the anatomy and function of three distinct small-field RGCs in the mouse.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Zhang, Y., Kim, I.-J., Sanes, J. R. & Meister, M. The most numerous ganglion cell type of the mouse retina is a selective feature detector. Proc. Natl Acad. Sci. USA 109, E2391–E2398 (2012).

    CAS  PubMed  Google Scholar 

  57. 57.

    Mani, A. & Schwartz, G. W. Circuit mechanism of a novel retinal ganglion cell with non-canonic receptive field structure. Curr. Biol. 27, 471–482 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Munch, T. A. et al. Approach sensitivity in the retina processed by a multifunctional neural circuit. Nat. Neurosci. 12, 1308–1316 (2009).

    CAS  PubMed  Google Scholar 

  59. 59.

    Jacoby, J. & Schwartz, G. W. Typology and circuitry of suppressed-by-contrast retinal ganglion cells. Front. Cell. Neurosci. 12, 269 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Sivyer, B., Taylor, W. R. & Vaney, D. I. Uniformity detector retinal ganglion cells fire complex spikes and receive only light-evoked inhibition. Proc. Natl Acad. Sci. USA 107, 5628–5633 (2010).

    CAS  PubMed  Google Scholar 

  61. 61.

    Lazzerini Ospri, L., Prusky, G. & Hattar, S. Mood, the circadian system, and melanopsin retinal ganglion cells. Annu. Rev. Neurosci. 40, 539–556 (2017).

    CAS  PubMed  Google Scholar 

  62. 62.

    Demb, J. B. & Singer, J. H. Intrinsic properties and functional circuitry of the AII amacrine cell. Vis. Neurosci. 29, 51–60 (2012).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Grimes, W. N., Zhang, J., Graydon, C. W., Kachar, B. & Diamond, J. S. Retinal parallel processors: more than 100 independent microcircuits operate within a single interneuron. Neuron 65, 873–885 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Haverkamp, S., Wassle, H. & Wässle, H. Characterization of an amacrine cell type of the mammalian retina immunoreactive for vesicular glutamate transporter 3. J. Comp. Neurol. 468, 251–263 (2004).

    CAS  PubMed  Google Scholar 

  65. 65.

    Lee, S. et al. An unconventional glutamatergic circuit in the retina formed by vGluT3 amacrine cells. Neuron 84, 708–715 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Kim, T., Soto, F. & Kerschensteiner, D. An excitatory amacrine cell detects object motion and provides feature-selective input to ganglion cells in the mouse retina. eLife 4, e08025 (2015).

    PubMed Central  Google Scholar 

  67. 67.

    Lee, S. et al. Segregated glycine-glutamate co-transmission from vGluT3 amacrine cells to contrast-suppressed and contrast-enhanced retinal circuits. Neuron 90, 27–34 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Masland, R. H. & Martin, P. R. The unsolved mystery of vision. Curr. Biol. 17, 577–582 (2007).

    Google Scholar 

  69. 69.

    Ramón y Cajal, S. La rétine des vertébrés [French]. La Cellule 9, 119–257 (1893).

    Google Scholar 

  70. 70.

    Wang, J., Jacoby, R. & Wu, S. M. Physiological and morphological characterization of ganglion cells in the salamander retina. Vis. Res. 119, 60–72 (2016).

    PubMed  Google Scholar 

  71. 71.

    Lisney, T. J., Wylie, D. R., Kolominsky, J. & Iwaniuk, A. N. Eye morphology and retinal topography in hummingbirds (Trochilidae: Aves). Brain Behav. Evol. 86, 176–190 (2015). Anatomical study on RGCs in the retinas of hummingbirds, with a key discussion of avian retinal organization in general.

    PubMed  Google Scholar 

  72. 72.

    Mitkus, M., Nevitt, G. A., Danielsen, J. & Kelber, A. Vision on the high seas: spatial resolution and optical sensitivity in two procellariiform seabirds with different foraging strategies. J. Exp. Biol. 219, 3329–3338 (2016).

    PubMed  Google Scholar 

  73. 73.

    Potier, S., Mitkus, M. & Kelber, A. High resolution of colour vision, but low contrast sensitivity in a diurnal raptor. Proc. R. Soc. Lond. B Biol. Sci. 29, 1885 (2018).

    Google Scholar 

  74. 74.

    Lettvin, J., Maturana, H., McCulloch, W. & Pitts, W. What the frog’s eye tells the frog’s brain. Proc. IRE 47, 1940–1951 (1959). Landmark article coining the idea that RGCs might be highly task specific. Put forward the notion of ‘bug detectors’.

    Google Scholar 

  75. 75.

    Collin, S. P. A web-based archive for topographic maps of retinal cell distribution in vertebrates: invited paper. Clin. Exp. Optom. 91, 85–95 (2008).

    PubMed  Google Scholar 

  76. 76.

    Mikelberg, F. S., Drance, S. M., Schulzer, M., Yidegiligne, H. M. & Weis, M. M. The normal human optic nerve: axon count and axon diameter distribution. Ophthalmology 96, 1325–1328 (1989).

    CAS  PubMed  Google Scholar 

  77. 77.

    Jeon, C. J., Strettoi, E. & Masland, R. H. The major cell populations of the mouse retina. J. Neurosci. 18, 8936–8946 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Johnston, J. & Lagnado, L. What the fish’s eye tells the fish’s brain. Neuron 76, 257–259 (2012).

    CAS  PubMed  Google Scholar 

  79. 79.

    Montgomery, G. How we see things that move. in Seeing, Hearing and Smelling the World. (Howard Hughes Medical Institute, 1995).

  80. 80.

    Peng, Y.-R. et al. Molecular classification and comparative taxonomics of foveal and peripheral cells in primate retina. Cell 176, 1222–1237 (2019). Study in primates demonstrating that foveal and peripheral circuits are molecularly distinct.

    CAS  PubMed  Google Scholar 

  81. 81.

    Sinha, R. et al. Cellular and circuit mechanisms shaping the perceptual properties of the primate fovea. Cell 168, 413–426 (2017). Study in primates exploring region-specific functional circuit motifs of the primate fovea.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Easter, Jr. S. S. & Nicola, G. N. The development of vision in the zebrafish (Danio rerio). Dev. Biol. 180, 646–663 (1996).

    CAS  PubMed  Google Scholar 

  83. 83.

    Li, Y. N., Tsujimura, T., Kawamura, S. & Dowling, J. E. Bipolar cell-photoreceptor connectivity in the zebrafish (Danio rerio) retina. J. Comp. Neurol. 520, 3786–3802 (2012).

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Lindsey, J., Ocko, S. A., Ganguli, S. & Deny, S. A unified theory of early visual representations from retina to cortex through anatomically constrained deep CNNs. In Proceedings of Seventh International Conference on Learning Representations (ICLR, 2019).

  85. 85.

    Inzunza, O., Bravo, H., Smith, R. L. & Angel, M. Topography and morphology of retinal ganglion cells in Falconiforms: a study on predatory and carrion-eating birds. Anat. Rec. 229, 271–277 (1991).

    CAS  PubMed  Google Scholar 

  86. 86.

    Bousfield, J. D. & Pessoa, V. F. Changes in ganglion cell density during post-metamorphic development in a neotropical tree frog Hyla raniceps. Vis. Res. 20, 501–510 (1980).

    CAS  PubMed  Google Scholar 

  87. 87.

    Lisney, T. J. & Collin, S. P. Retinal ganglion cell distribution and spatial resolving power in elasmobranchs. Brain Behav. Evol. 72, 59–77 (2008).

    PubMed  Google Scholar 

  88. 88.

    Ding, H., Smith, R. G., Poleg-Polsky, A., Diamond, J. S. & Briggman, K. L. Species-specific wiring for direction selectivity in the mammalian retina. Nature 535, 105–110 (2016). Study on mice and rabbits demonstrating that in these species retinal circuits for direction selectivity use distinct dendritic wiring motifs to acknowledge differences in eye sizes.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Pettigrew, J. D., Bhagwandin, A., Haagensen, M. & Manger, P. R. Visual acuity and heterogeneities of retinal ganglion cell densities and the tapetum lucidum of the African elephant (Loxodonta africana). Brain Behav. Evol. 75, 251–261 (2010).

    PubMed  Google Scholar 

  90. 90.

    Linsenmeier, R. A. & Zhang, H. F. Retinal oxygen: from animals to humans. Prog. Retin. Eye Res. 58, 115–151 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Vaiman, M., Abuita, R. & Bekerman, I. Optic nerve sheath diameters in healthy adults measured by computer tomography. Int. J. Ophthalmol. 8, 1240–1244 (2015).

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Robles, E., Laurell, E. & Baier, H. The retinal projectome reveals brain-area-specific visual representations generated by ganglion cell diversity. Curr. Biol. 24, 2085–2096 (2014). Study on larval zebrafish showing that RGCs with similar dendritic stratification profiles can exhibit very distinct central wiring motifs.

    CAS  PubMed  Google Scholar 

  93. 93.

    Antinucci, P., Suleyman, O., Monfries, C. & Hindges, R. Neural mechanisms generating orientation selectivity in the retina. Curr. Biol. 26, 1802–1815 (2016). Study on larval zebrafish showing that orientation-selective computations begin at the level of bipolar cell interactions with specific amacrine cells.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Johnston, J. et al. A retinal circuit generating a dynamic predictive code for oriented features. Neuron 102, 1211–1222 (2019). Study on larval zebrafish extending the results from Antinucci et al. (2016) to show how distinct orientation-selective inputs from bipolar cells lead to the possibility to build highly complex response properties at the level of RGCs.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Hildebrand, D. G. C. et al. Whole-brain serial-section electron microscopy in larval zebrafish. Nature 545, 345–349 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Faisal, A. A., White, J. A. & Laughlin, S. B. Ion-channel noise places limits on the miniaturization of the brain’s wiring. Curr. Biol. 15, 1143–1149 (2005).

    CAS  PubMed  Google Scholar 

  97. 97.

    Faisal, A. A. & Laughlin, S. B. Stochastic simulations on the reliability of action potential propagation in thin axons. PLOS Comput. Biol. 3, e79 (2007).

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Baden, T. & Osorio, D. The retinal basis of vertebrate color vision. Annu. Rev. Vis. Sci. 5, 177–200 (2019).

    CAS  PubMed  Google Scholar 

  99. 99.

    Kelber, A. & Osorio, D. From spectral information to animal colour vision: experiments and concepts. Proc. R. Soc. B Biol. Sci. 277, 1617–1625 (2010).

    Google Scholar 

  100. 100.

    Theiss, S. M., Davies, W. I. L., Collin, S. P., Hunt, D. M. & Hart, N. S. Cone monochromacy and visual pigment spectral tuning in wobbegong sharks. Biol. Lett. 8, 1019–1022 (2012).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Peichl, L. Diversity of mammalian photoreceptor properties: adaptations to habitat and lifestyle? Anat. Rec. A Discov. Mol. Cell. Evol. Biol. 287, 1001–1012 (2005).

    PubMed  Google Scholar 

  102. 102.

    Rocha, F. A. F., Saito, C. A., Silveira, L. C. L., De Souza, J. M. & Ventura, D. F. Twelve chromatically opponent ganglion cell types in turtle retina. Vis. Neurosci. 25, 307–315 (2008).

    CAS  PubMed  Google Scholar 

  103. 103.

    Marshak, D. W. & Mills, S. L. Short-wavelength cone-opponent retinal ganglion cells in mammals. Vis. Neurosci. 31, 165–175 (2014).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Kalinina, A. V. Quantity and topography of frog’s retinal ganglion cells. Vis. Res. 16, 929–934 (1976).

    CAS  PubMed  Google Scholar 

  105. 105.

    Buchsbaum, G. & Gottschalk, a Trichromacy, opponent colours coding and optimum colour information transmission in the retina. Proc. R. Soc. Lond. B. Biol. Sci. 220, 89–113 (1983).

    CAS  PubMed  Google Scholar 

  106. 106.

    Lewis, A. & Zhaoping, L. Are cone sensitivities determined by natural color statistics? J. Vis. 6, 285–302 (2006).

    PubMed  Google Scholar 

  107. 107.

    Osorio, D. & Vorobyev, M. A review of the evolution of animal colour vision and visual communication signals. Vis. Res. 48, 2042–2051 (2008).

    CAS  PubMed  Google Scholar 

  108. 108.

    Hughes, A. Topographical relationships between the anatomy and physiology of the rabbit visual system. Doc. Ophthalmol. 30, 33–159 (1971).

    CAS  PubMed  Google Scholar 

  109. 109.

    Sherman, S. M. Visual fields of cats with cortical and tectal lesions. Science 185, 355–357 (1974).

    CAS  PubMed  Google Scholar 

  110. 110.

    Meyer, A. F., Poort, J., O’Keefe, J., Sahani, M. & Linden, J. F. A head-mounted camera system integrates detailed behavioral monitoring with multichannel electrophysiology in freely moving mice. Neuron 100, 46–60 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Wallace, D. J. et al. Rats maintain an overhead binocular field at the expense of constant fusion. Nature 498, 65–69 (2013).

    CAS  PubMed  Google Scholar 

  112. 112.

    Mitkus, M., Olsson, P., Toomey, M. B., Corbo, J. C. & Kelber, A. Specialized photoreceptor composition in the raptor fovea. J. Comp. Neurol. 529, 2152–2163 (2017). Study showing that the central but not the temporal foveas of raptors tend to lack the double cones that are traditionally associated with achromatic high-spatial-acuity vision. These birds might therefore use high-resolution tetrachromatic vision for high-spatial-acuity tasks.

    Google Scholar 

  113. 113.

    Pettigrew, J. D., Collin, S. P. & Ott, M. Convergence of specialised behaviour, eye movements and visual optics in the sandlance (Teleostei) and the chameleon (Reptilia). Curr. Biol. 9, 421–424 (1999).

    CAS  PubMed  Google Scholar 

  114. 114.

    Rucci, M. & Poletti, M. Control and functions of fixational eye movements. Annu. Rev. Vis. Sci. 1, 499–518 (2015).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Samonds, J. M., Geisler, W. S. & Priebe, N. J. Natural image and receptive field statistics predict saccade sizes. Nat. Neurosci. 21, 1591–1599 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Manookin, M. B., Patterson, S. S. & Linehan, C. M. Neural mechanisms mediating motion sensitivity in parasol ganglion cells of the primate retina. Neuron 97, 1327–1340 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Yilmaz, M. & Meister, M. rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).

    CAS  PubMed  Google Scholar 

  118. 118.

    Janssen, J. Searching for zooplankton just outside Snell’s window. Limmol.Oceanogr. 26, 1168–1171 (1981).

    Google Scholar 

  119. 119.

    Peichl, L. Die Augen der Säugetiere: unterschiedliche Blicke in die Welt. Biol. Unserer Zeit 27, 96–105 (1997).

    Google Scholar 

  120. 120.

    Hughes, A. A comparison of retinal ganglion cell topography in the plains and tree kangaroo. J. Physiol. 244, 61P–63P (1975).

    CAS  PubMed  Google Scholar 

  121. 121.

    Sablin, M. V. & Khlopachev, G. A. The earliest ice age dogs: evidence from Eliseevichi 1. Curr. Anthropol. 43, 795–799 (2002).

    Google Scholar 

  122. 122.

    Peichl, L. Topography of ganglion-cells in the dog and wolf retina. J. Comp. Neurol. 324, 603–620 (1992).

    CAS  PubMed  Google Scholar 

  123. 123.

    Coimbra, J. P. & Manger, P. R. Retinal ganglion cell topography and spatial resolving power in the white rhinoceros (Ceratotherium simum). J. Comp. Neurol. 525, 2484–2498 (2017).

    PubMed  Google Scholar 

  124. 124.

    Coimbra, J. P., Bertelsen, M. F. & Manger, P. R. Retinal ganglion cell topography and spatial resolving power in the river hippopotamus (Hippopotamus amphibius). J. Comp. Neurol. 525, 2499–2513 (2017).

    PubMed  Google Scholar 

  125. 125.

    Collin, S. P. Behavioural ecology and retinal cell topography. in Adaptive Mechanisms in the Ecology of Vision (eds Archer S. N. et al.) 509–535 (Springer, 1999).

  126. 126.

    Coimbra, J. P., Collin, S. P. & Hart, N. S. Topographic specializations in the retinal ganglion cell layer correlate with lateralized visual behavior, ecology, and evolution in cockatoos. J. Comp. Neurol. 522, 3363–3385 (2014).

    PubMed  Google Scholar 

  127. 127.

    Tucker, V. A. The deep fovea, sideways vision and spiral flight paths in raptors. J. Exp. Biol. 203, 3745–3754 (2000).

    CAS  PubMed  Google Scholar 

  128. 128.

    Kolb, H. & Marshak, D. The midget pathways of the primate retina. Doc. Ophthalmol. 106, 67–81 (2003).

    PubMed  Google Scholar 

  129. 129.

    Baudin, J., Angueyra, J. M., Sinha, R. & Rieke, F. S-cone photoreceptors in the primate retina are functionally distinct from L and M cones. Elife 8, e39166 (2019).

    PubMed  PubMed Central  Google Scholar 

  130. 130.

    Harvey, B. M. & Dumoulin, S. O. The relationship between cortical magnification factor and population receptive field size in human visual cortex: constancies in cortical architecture. J. Neurosci. 31, 13604–13612 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Szél, A. et al. Unique topographic separation of two spectral classes of cones in the mouse retina. J. Comp. Neurol. 325, 327–342 (1992).

    PubMed  Google Scholar 

  132. 132.

    Röhlich, P., van Veen, T. & Szél, A. Two different visual pigments in one retinal cone cell. Neuron 13, 1159–1166 (1994).

    PubMed  Google Scholar 

  133. 133.

    Haverkamp, S. et al. The primordial, blue-cone color system of the mouse retina. J Neurosci 25, 5438–5445 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Tan, Z., Sun, W., Chen, T.-W., Kim, D. & Ji, N. Neuronal representation of ultraviolet visual stimuli in mouse primary visual cortex. Sci. Rep. 5, 12597 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135.

    Denman, D. J. et al. Mouse color and wavelength-specific luminance contrast sensitivity are non-uniform across visual space. Elife 7, e31209 (2018).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Joesch, M. & Meister, M. A neuronal circuit for colour vision based on rod–cone opponency. Nature 532, 236–239 (2016).

    CAS  PubMed  Google Scholar 

  137. 137.

    Chang, L., Breuninger, T. & Euler, T. Chromatic coding from cone-type unselective circuits in the mouse retina. Neuron 77, 559–571 (2013).

    CAS  PubMed  Google Scholar 

  138. 138.

    Kim, I.-J., Zhang, Y., Yamagata, M., Meister, M. & Sanes, J. R. Molecular identification of a retinal cell type that responds to upward motion. Nature 452, 478–482 (2008).

    CAS  PubMed  Google Scholar 

  139. 139.

    Peichl, L. & Ott, H. & Boycott, B. B. Alpha ganglion cells in mammalian retinae. Proc. R. Soc. Lond. Ser. B. Biol. Sci. 231, 169–197 (1987).

    CAS  Google Scholar 

  140. 140.

    Barlow, H. B., Hill, R. M. & Levick, W. R. Rabbit retinal ganglion cells responding selectively to direction and speed of image motion in the rabbit. J. Physiol. 173, 377–407 (1964).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Krapp, H. G. & Hengstenberg, R. Estimation of self-motion by optic flow processing in single visual interneurons. Nature 384, 463–466 (1996).

    CAS  PubMed  Google Scholar 

  142. 142.

    Yu, W. Q. et al. synaptic convergence patterns onto retinal ganglion cells are preserved despite topographic variation in pre- and postsynaptic territories. Cell Rep. 25, 2017–2026 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. 143.

    Seung, H. S. S. & Sümbül, U. Neuronal cell types and connectivity: lessons from the retina. Neuron 83, 1262–1272 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Vlasits, A. L., Euler, T. & Franke, K. Function first: classifying cell types and circuits of the retina. Curr. Opin. Neurobiol. 56, 8–15 (2019).

    CAS  PubMed  Google Scholar 

  145. 145.

    Laughlin, S. Matching coding to scenes to enhance efficiency. in Physical and Biological Processing of Images (eds Braddick O. J. & Sleigth A. C.) 42–52 (Springer, 1983).

  146. 146.

    Atick, J. J. & Redlich, A. N. Towards a theory of early visual processing. Neural. Comput. 2, 308–320 (2008).

    Google Scholar 

  147. 147.

    Simoncelli, E. P. & Olshausen, B. A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

    CAS  PubMed  Google Scholar 

  148. 148.

    van der Schaaf, A. & van Hateren, J. H. Modelling the power spectra of natural images: statistics and information. Vis. Res. 36, 2759–2770 (1996).

    PubMed  Google Scholar 

  149. 149.

    Atick, J. J. & Redlich, A. N. What does the retina know about natural scenes? Neural. Comput. 4, 196–210 (1992).

    Google Scholar 

  150. 150.

    Doi, E. et al. Efficient coding of spatial information in the primate retina. J. Neurosci. 32, 16256–16264 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Sinz, F. & Bethge, M. Temporal adaptation enhances efficient contrast gain control on Natural Images. PLOS Comput. Biol. 9, e1002889 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Pitkow, X. & Meister, M. Decorrelation and efficient coding by retinal ganglion cells. Nat. Neurosci. 15, 628–635 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. 153.

    Dacey, D. M. Primate retina: cell types, circuits and color opponency. Prog. Retin. Eye Res. 18, 737–763 (1999).

    CAS  PubMed  Google Scholar 

  154. 154.

    Rheaume, B. A. et al. Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes. Nat. Commun. 9, 2759 (2018).

    PubMed  PubMed Central  Google Scholar 

  155. 155.

    Gjorgjieva, J., Sompolinsky, H. & Meister, M. Benefits of pathway splitting in sensory coding. J. Neurosci. 34, 12127–12144 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156.

    Kastner, D. B., Baccus, S. A. & Sharpee, T. O. Critical and maximally informative encoding between neural populations in the retina. Proc. Natl Acad. Sci. USA 112, 2533–2538 (2015).

    CAS  PubMed  Google Scholar 

  157. 157.

    Ocko, S. A., Lindsey, J., Ganguli, S. & Deny, S. The emergence of multiple retinal cell types through efficient coding of natural movies. Preprint at bioRxiv (2018).Theoretical study showing that a small number of RGC types with simple centre–surround receptive fields can be principally explained by the statistics of natural scenes.

  158. 158.

    Gollisch, T. & Meister, M. Review eye smarter than scientists believed: neural computations in circuits of the retina. Neuron 65, 150–164 (2009).

    Google Scholar 

  159. 159.

    Turner, M. H., Sanchez Giraldo, L. G., Schwartz, O. & Rieke, F. Stimulus- and goal-oriented frameworks for understanding natural vision. Nat. Neurosci. 22, 15–24 (2019).

    CAS  PubMed  Google Scholar 

  160. 160.

    Turner, M. H., Schwartz, G. W. & Rieke, F. Receptive field center-surround interactions mediate context-dependent spatial contrast encoding in the retina. eLife 7, e38841 (2018).

    PubMed  PubMed Central  Google Scholar 

  161. 161.

    Schwartz, G. W. et al. The spatial structure of a nonlinear receptive field. Nat. Neurosci. 15, 1572–1580 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. 162.

    Freeman, J. et al. Mapping nonlinear receptive field structure in primate retina at single cone resolution. eLife 4, 284–299 (2015).

    Google Scholar 

  163. 163.

    Liu, J. K. et al. Spike-triggered covariance analysis reveals phenomenological diversity of contrast adaptation in the retina. PLOS Comput. Biol. 11, e1004425 (2015).

    PubMed  PubMed Central  Google Scholar 

  164. 164.

    Real, E., Asari, H., Gollisch, T. & Meister, M. Neural circuit inference from function to structure. Curr. Biol. 27, 189–198 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. 165.

    McIntosh, L. T., Maheswaranathan, N., Nayebi, A., Ganguli, S. & Baccus, S. A. Deep learning models of the retinal response to natural scenes. Adv. Neural. Inf. Process. Syst. 29, 1369–1377 (2016).

    PubMed  PubMed Central  Google Scholar 

  166. 166.

    Maheswaranathan, N., Kastner, D. B., Baccus, S. A. & Ganguli, S. Inferring hidden structure in multilayered neural circuits. PLOS Comput. Biol. 14, e1006291 (2018).

    PubMed  PubMed Central  Google Scholar 

  167. 167.

    Tkačik, G. et al. Natural images from the birthplace of the human eye. PLOS ONE 6, e20409 (2011).

    PubMed  PubMed Central  Google Scholar 

  168. 168.

    Tedore, C. & Nilsson, D. E. Avian UV vision enhances leaf surface contrasts in forest environments. Nat. Commun. 10, 238 (2019).

    PubMed  PubMed Central  Google Scholar 

  169. 169.

    Nevala, N. E. & Baden, T. A low-cost hyperspectral scanner for natural imaging and the study of animal colour vision above and under water. Sci. Rep. 9, 10799 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. 170.

    Zeil, J., Boeddeker, N. & Hemmi, J. M. Vision and the organization of behaviour. Curr. Biol. 18, R320–R323 (2008).

    CAS  PubMed  Google Scholar 

  171. 171.

    Lamb, T. D., Collin, S. P., Pugh, E. N. Jr. Evolution of the vertebrate eye: opsins, photoreceptors, retina and eye cup. Nat. Rev. Neurosci. 8, 960–976 (2007). Key account of the vertebrate eye’s evolutionary history.

    CAS  PubMed  PubMed Central  Google Scholar 

  172. 172.

    Young, G. C. Early evolution of the vertebrate eye —fossil evidence. Evol. Educ. Outreach 1, 427–438 (2008).

    Google Scholar 

  173. 173.

    Fritzsch, B. & Collin, S. P. Dendritic distribution of two populations of ganglion cells and the retinopetal fibers in the retina of the silver lamprey (Ichthyomyzon unicuspis). Vis. Neurosci. 4, 533–545 (1990).

    CAS  PubMed  Google Scholar 

  174. 174.

    Morris, S. C. & Caron, J.-B. A primitive fish from the Cambrian of North America. Nature 512, 419–422 (2014).

    CAS  PubMed  Google Scholar 

  175. 175.

    Xian-Guang, H., Aldridge, R. J., Siveter, D. J., Siveter, D. J. & Xiang-Hong, F. New evidence on the anatomy and phylogeny of the earliest vertebrates. Proc. R. Soc. B Biol. Sci. 269, 1865–1869 (2002).

    Google Scholar 

  176. 176.

    Shu, D. G. et al. Lower Cambrian vertebrates from south China. Nature 402, 42–46 (1999).

    CAS  Google Scholar 

  177. 177.

    Fletcher, L. N. et al. Classification of retinal ganglion cells in the southern hemisphere lamprey Geotria australis (Cyclostomata). J. Comp. Neurol. 522, 750–771 (2014).

    PubMed  Google Scholar 

  178. 178.

    Collin, S. P., Davies, W. L., Hart, N. S. & Hunt, D. M. The evolution of early vertebrate photoreceptors. Phil. Trans. R. Soc. B Biol. Sci. 364, 2925–2940 (2009).

    CAS  Google Scholar 

  179. 179.

    Sallan, L., Friedman, M., Sansom, R. S., Bird, C. M. & Sansom, I. J. The nearshore cradle of early vertebrate diversification. Science 362, 460–464 (2018).

    CAS  PubMed  Google Scholar 

  180. 180.

    Brazeau, M. D. & Friedman, M. The origin and early phylogenetic history of jawed vertebrates. Nature 520, 490–497 (2015).

    PubMed  PubMed Central  Google Scholar 

  181. 181.

    Country, M. W. Retinal metabolism: a comparative look at energetics in the retina. Brain Res. 1672, 50–57 (2017).

    CAS  PubMed  Google Scholar 

  182. 182.

    Wright, A. F., Chakarova, C. F., Abd El-Aziz, M. M. & Bhattacharya, S. S. Photoreceptor degeneration: genetic and mechanistic dissection of a complex trait. Nat. Rev. Genet. 11, 273–284 (2010).

    CAS  PubMed  Google Scholar 

  183. 183.

    Krishnan, J. & Rohner, N. Cavefish and the basis for eye loss. Phil. Trans. R. Soc. B Biol. Sci. 372, 20150487 (2017).

    Google Scholar 

  184. 184.

    Gore, A. V. et al. An epigenetic mechanism for cavefish eye degeneration. Nat. Ecol. Evol. 2, 1155–1160 (2018).

    PubMed  PubMed Central  Google Scholar 

  185. 185.

    Merriman, D. K., Sajdak, B. S., Li, W. & Jones, B. W. Seasonal and post-trauma remodeling in cone-dominant ground squirrel retina. Exp. Eye Res. 150, 90–105 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  186. 186.

    Emran, F., Rihel, J., Adolph, A. R. & Dowling, J. E. Zebrafish larvae lose vision at night. Proc. Natl Acad. Sci. USA 107, 6034–6039 (2010).

    CAS  PubMed  Google Scholar 

  187. 187.

    Adolph, A. R. Temporal tuning and nonlinearity of intraretinal pathways in turtle: effects of temperature, stimulus intensity, and size. Biol. Cybern. 52, 59–69 (1985).

    CAS  PubMed  Google Scholar 

  188. 188.

    Ankel-Simons, F. & Rasmussen, D. T. Diurnality, nocturnality, and the evolution of primate visual systems. Am. J. Phys. Anthropol. 47, 100–117 (2008).

    PubMed  Google Scholar 

  189. 189.

    Cronin, T. W. & Bok, M. J. Photoreception and vision in the ultraviolet. J. Exp. Biol. 219, 2790–2801 (2016).

    PubMed  Google Scholar 

  190. 190.

    Muaddi, J. A. & Jamal, M. A. Solar spectrum at depth in water. Renew. Energy 1, 31–35 (1991).

    Google Scholar 

  191. 191.

    Williams, R. W., Strom, R. C. & Goldowitz, D. Natural variation in neuron number in mice is linked to a major quantitative trait locus on Chr 11. J. Neurosci. 18, 138–146 (2018).

    Google Scholar 

  192. 192.

    Kolb H. in Webvision: The Organization of the Retina and Visual System (eds Kolb, H. et al.) (Univ. Utah Health Sciences Center, 1995).

  193. 193.

    McMains, E., Krishnan, V., Prasad, S. & Gleason, E. Expression and localization of CLC chloride transport proteins in the avian retina. PLOS ONE 6, e17647 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  194. 194.

    Gramage, E., Li, J. & Hitchcock, P. The expression and function of midkine in the vertebrate retina. Br. J. Pharmacol. 171, 913–923 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  195. 195.

    Almeida, A. D. et al. Spectrum of fates: a new approach to the study of the developing zebrafish retina. Development 141, 1971–1980 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  196. 196.

    Deng, P. et al. Localization of neurotransmitters and calcium binding proteins to neurons of salamander and mudpuppy retinas. Vis. Res. 41, 1771–1783 (2001).

    CAS  PubMed  Google Scholar 

  197. 197.

    Holmberg, K. Fine structure of the optic tract in the Atlantic hagfish, Myxine glutinosa. Acta Zool. 53, 165–171 (1972).

    Google Scholar 

  198. 198.

    Pita, D., Moore, B. A., Tyrrell, L. P. & Fernández-Juricic, E. Vision in two cyprinid fish: implications for collective behavior. PeerJ 3, e1113 (2015).

    PubMed  PubMed Central  Google Scholar 

  199. 199.

    Dalton, B. E., de Busserolles, F., Marshall, N. J. & Carleton, K. L. Retinal specialization through spatially varying cell densities and opsin coexpression in cichlid fish. J. Exp. Biol. 220, 266–277 (2017).

    PubMed  PubMed Central  Google Scholar 

  200. 200.

    Wagner, H. J., Fröhlich, E., Negishi, K. & Collin, S. P. The eyes of deep-sea fish II. Functional morphology of the retina. Prog. Retin. Eye Res. 17, 637–685 (1998).

    CAS  PubMed  Google Scholar 

  201. 201.

    Hitchcock, P. & Easter, S. Retinal ganglion cells in goldfish: a qualitative classification into four morphological types, and a quantitative study of the development of one of them. J. Neurosci. 6, 1037–1050 (1986).

    CAS  PubMed  PubMed Central  Google Scholar 

  202. 202.

    Dunlop, S. A. & Beazley, L. D. Changing retinal ganglion cell distribution in the frog Heleioporus eyrei. J. Comp. Neurol. 202, 221–236 (1981).

    CAS  PubMed  Google Scholar 

  203. 203.

    Nguyen, V. S. & Straznicky, C. The development and the topographic organization of the retinal ganglion cell layer in Bufo marinus. Exp. Brain Res. 75, 345–353 (1989).

    CAS  PubMed  Google Scholar 

  204. 204.

    Graydon, M. L. & Giorgi, P. P. Topography of the retinal ganglion cell layer of Xenopus. J. Anat. 139, 145–157 (1984).

    PubMed  PubMed Central  Google Scholar 

  205. 205.

    Zhang, J., Yang, Z. & Wu, S. M. Immuocytochemical analysis of spatial organization of photoreceptors and amacrine and ganglion cells in the tiger salamander retina. Vis. Neurosci. 21, 157–166 (2004).

    PubMed  Google Scholar 

  206. 206.

    Pushchin, I. I. & Karetin, Y. A. Retinal ganglion cells in the eastern newt Notophthalmus viridescens: Topography, morphology, and diversity. J. Comp. Neurol. 516, 533–552 (2009).

    PubMed  Google Scholar 

  207. 207.

    Hauzman, E., Bonci, D. M. O. & Ventura, D. F. in Retinal Topographic Maps: A Glimpse into the Animals’ Visual World, Sensory Nervous System (ed. Heinbockel T.) (IntechOpen, 2018).

  208. 208.

    Nagloo, N., Collin, S. P., Hemmi, J. M. & Hart, N. S. Spatial resolving power and spectral sensitivity of the saltwater crocodile, Crocodylus porosus, and the freshwater crocodile, Crocodylus johnstoni. J. Exp. Biol. 219, 1394–1404 (2016).

    PubMed  Google Scholar 

  209. 209.

    Hassni, M. El, M’hamed, S. B., Reṕerant, J. & Bennis, M. Quantitative and topographical study of retinal ganglion cells in the chameleon (Chameleo chameleon). Brain Res. Bull. 44, 621–625 (1997).

    PubMed  Google Scholar 

  210. 210.

    Bennis, M. et al. A quantitative ultrastructural study of the optic nerve of the chameleon. Brain Behav. Evol. 58, 49–60 (2001).

    CAS  PubMed  Google Scholar 

  211. 211.

    New, S. T. D. & Bull, C. M. Retinal ganglion cell topography and visual acuity of the sleepy lizard (Tiliqua rugosa). J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 197, 703–709 (2011).

    PubMed  Google Scholar 

  212. 212.

    Hayes, B. P. & Brooke, M. D. L. Retinal ganglion cell distribution and behaviour in procellariiform seabirds. Vis. Res. 30, 1277–1289 (1990).

    CAS  PubMed  Google Scholar 

  213. 213.

    Boire, D., Dufour, J. S., Théoret, H. & Ptito, M. Quantitative analysis of the retinal ganglion cell layer in the ostrich, Struthio camelus. Brain Behav. Evol. 58, 343–355 (2001).

    CAS  PubMed  Google Scholar 

  214. 214.

    Suburo, A. M., Herrero, M. V. & Scolaro, J. A. Regionalization of the ganglion cell layer in the retina of the Magellanic penguin (Spheniscus magellanicus). Colon. Waterbirds 14, 17 (1991).

    Google Scholar 

  215. 215.

    Coimbra, J. P., Nolan, P. M., Collin, S. P. & Hart, N. S. Retinal ganglion cell topography and spatial resolving power in penguins. Brain Behav. Evol. 80, 254–268 (2012).

    PubMed  Google Scholar 

  216. 216.

    Wathey, J. C. & Pettigrew, J. D. Quantitative analysis of the retinal ganglion cell layer and optic nerve of the barn owl Tyto alba. Brain Behav. Evol. 33, 279–292 (1989).

    CAS  PubMed  Google Scholar 

  217. 217.

    Lisney, T. J., Iwaniuk, A. N., Bandet, M. V. & Wylie, D. R. Eye shape and retinal topography in owls (Aves: Strigiformes). Brain Behav. Evol. 79, 218–236 (2012).

    PubMed  Google Scholar 

  218. 218.

    Bravo, H. & Pettigrew, J. D. The distribution of neurons projecting from the retina and visual cortex to the thalamus and tectum opticum of the barn owl, Tyto alba, and the burrowing owl, Speotyto cunicularia. J. Comp. Neurol. 199, 419–441 (1981).

    CAS  PubMed  Google Scholar 

  219. 219.

    Lisney, T. J. et al. Interspecifc variation in eye shape and retinal topography in seven species of galliform bird (Aves: Galliformes: Phasianidae). J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 198, 717–731 (2012).

    PubMed  Google Scholar 

  220. 220.

    Lisney, T. J. et al. Ecomorphology of eye shape and retinal topography in waterfowl (Aves: Anseriformes: Anatidae) with different foraging modes. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 199, 385–402 (2013).

    PubMed  Google Scholar 

  221. 221.

    Hart, N. S. Vision in the peafowl (Aves: Pavo cristatus). J. Exp. Biol. 205, 3925–3935 (2002).

    Google Scholar 

  222. 222.

    Moore, B. A., Pita, D., Tyrrell, L. P. & Fernandez-Juricic, E. Vision in avian emberizid foragers: maximizing both binocular vision and fronto-lateral visual acuity. J. Exp. Biol. 218, 1347–1358 (2015).

    PubMed  Google Scholar 

  223. 223.

    Moore, B. A., Doppler, M., Young, J. E. & Fernández-Juricic, E. Interspecific differences in the visual system and scanning behavior of three forest passerines that form heterospecific flocks. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 199, 263–277 (2013).

    PubMed  Google Scholar 

  224. 224.

    Coimbra, J. P., Collin, S. P. & Hart, N. S. Topographic specializations in the retinal ganglion cell layer of Australian passerines. J. Comp. Neurol. 522, 3609–3628 (2014).

    CAS  PubMed  Google Scholar 

  225. 225.

    Hayes, B. P. & Holden, A. L. The distribution of displaced ganglion cells in the retina of the pigeon. Exp. Brain Res. 49, 181–188 (1983).

    CAS  PubMed  Google Scholar 

  226. 226.

    Coimbra, J. P., Videira Marceliano, M. L., Da Silveira Andrade-Da-Costa, B. L. & Yamada, E. S. The retina of tyrant flycatchers: Topographic organization of neuronal density and size in the ganglion cell layer of the great kiskadee Pitangus sulphuratus and the rusty margined flycatcher Myiozetetes cayanensis (Aves: Tyrannidae). Brain Behav. Evol. 68, 15–25 (2006).

    PubMed  Google Scholar 

  227. 227.

    Coimbra, J. P. et al. Number and distribution of neurons in the retinal ganglion cell layer in relation to foraging behaviors of tyrant flycatchers. J. Comp. Neurol. 514, 66–73 (2009).

    PubMed  Google Scholar 

  228. 228.

    Krabichler, Q., Vega-Zuniga, T., Morales, C., Luksch, H. & Marín, G. J. The visual system of a palaeognathous bird: visual field, retinal topography and retino-central connections in the Chilean tinamou (Nothoprocta perdicaria). J. Comp. Neurol. 523, 226–250 (2015).

    PubMed  Google Scholar 

  229. 229.

    Moroney, M. K. & Pettigrew, J. D. Some observations on the visual optics of kingfishers (Aves, Coraciformes, Alcedinidae). J. Comp. Physiol. A 160, 137–149 (1987).

    Google Scholar 

  230. 230.

    Do-Nascimento, J. L., Do-Nascimento, R. S., Damasceno, B. A. & Silveira, L. C. The neurons of the retinal ganglion cell layer of the guinea pig: quantitative analysis of their distribution and size. Braz. J. Med. Biol. Res. 24, 199–214 (1991).

    CAS  PubMed  Google Scholar 

  231. 231.

    Coimbra, J. P., Hart, N. S., Collin, S. P. & Manger, P. R. Scene from above: retinal ganglion cell topography and spatial resolving power in the giraffe (Giraffa camelopardalis). J. Comp. Neurol. 521, 2042–2057 (2013).

    PubMed  Google Scholar 

  232. 232.

    Mass, A. M. Visual field organization and retinal resolution in the beluga whale Delphinapterus leucas (Pallas). Dokl. Biol. Sci. 381, 555–558 (2001).

    CAS  PubMed  Google Scholar 

  233. 233.

    Schall, J. D., Perry, V. H. & Leventhal, A. G. Ganglion cell dendritic structure and retinal topography in the rat. J. Comp. Neurol. 257, 160–165 (1987).

    CAS  PubMed  Google Scholar 

  234. 234.

    Curcio, C. A. & Allen, K. A. Topography of ganglion cells in human retina. J. Comp. Neurol. 300, 5–25 (1990).

    CAS  PubMed  Google Scholar 

  235. 235.

    Stabio, M. E. et al. A novel map of the mouse eye for orienting retinal topography in anatomical space. J. Comp. Neurol. 526, 1749–1759 (2018).

    PubMed  PubMed Central  Google Scholar 

  236. 236.

    Szél, A. & Roehlich, P. Two cone types of rat retina detected by anti-visual pigment antibodies. Exp. Eye Res. 55, 47–52 (1992).

    PubMed  Google Scholar 

  237. 237.

    Liu, J. K. et al. Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization. Nat. Commun. 8, 149 (2017).

    PubMed  PubMed Central  Google Scholar 

Download references


The authors thank L. Peichl, J. Coimbra, T. Lisney and S. Collin for very helpful discussions as well as the four anonymous reviewers for their insightful comments. T.B. also acknowledges support from the FENS-Kavli Network of Excellence and from the EMBO Young Investigator Programme. Funding was provided by the European Research Council (Starting Grant NeuroVisEco 677687, T.B.), UK Research and Innovation (Biotechnology and Biological Sciences Research Council, BB/R014817/1, and Medical Research Council, MC_PC_15071, T.B.), the Leverhulme Trust (PLP-2017-005, T.B.), the Lister Institute for Preventive Medicine (T.B.), the German Research Foundation (SFB 1233 — project number 276693517, T.E. and P.B.; SPP 2041: EU42/9-1, T.E.; BE5601/2, P.B.; BE5601/4, P.B.), the National Eye Institute (1R01EY023766-01A1, T.E.), and the German Ministry for Education and Research (FKZ 01GQ1601, P.B.).

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Correspondence to Tom Baden.

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Supplementary information


Visual field

The area in space that an animal can simultaneously survey using its eyes.

Efficient coding hypothesis

A theory that posits that the retina has evolved to encode the visual environment efficiently: that is, by minimizing the redundancy in the information carried by different neurons.

Centre–surround receptive fields

An area in visual space or on the retinal surface where presentation of a stimulus in the receptive field centre excites the neuron and presentation of the same stimulus in the receptive field surround (a typically larger and concentric area) instead suppresses the neuron.


Groups of species that share a phylogenetic branch.

Midget pathway

A circuit motif found in the primate retina consisting of cone photoreceptors, midget bipolar cells and midget retinal ganglion cells. A distinguishing feature of the midget pathway is that it exhibits a 1:1:1 connectivity from cones to retinal ganglion cells at the foveal centre.

Deep neural networks

Machine learning algorithms consisting of many processing layers that combine linear operations such as convolutions with non-linear stages such as rectification. Such networks have been shown to achieve human-like performance in many visual tasks.

Visual angle

The angle that encompasses a certain feature in the visual world, from the point of view of an animal’s eye.

Colour-opponent RGCs

Retinal ganglion cells (RGCs) that are excited by the presentation of light at one range of wavelengths and suppressed by presentation of light at another range of wavelengths.

Binocular region

The region in the visual space that is simultaneously surveyed by both eyes.

Goal-directed saccades

Rapid eye movements that bring specific objects into a retinal region’s field of view.

Power spectrum

A representation of the energy in each of the frequency components in an image. It can be computed using a Fourier transform.

Linear model

A model in which neurons exclusively perform linear operations such as forming weighted sums of inputs, without any non-linearities, such as thresholding.

Cost function

A mathematical function that assigns a cost to a state of the world, an action or a representation and therefore measures its quality. Examples include the mean squared error, which measures how well the representation of an image would allow it to be reconstructed.

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Baden, T., Euler, T. & Berens, P. Understanding the retinal basis of vision across species. Nat Rev Neurosci 21, 5–20 (2020).

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