Understanding the retinal basis of vision across species

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Abstract

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.

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Acknowledgements

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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Glossary

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.

Clades

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 (2019) doi:10.1038/s41583-019-0242-1

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