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      Functional connectivity in the retina at the resolution of photoreceptors

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          Abstract

          To understand a neural circuit requires knowing its connectivity. This paper reports measurements of functional connectivity between the input and ouput layers of the retina at single cell resolution and its implications for color vision. Multi-electrode technology was employed to record simultaneously from complete populations of the retinal ganglion cell types (midget, parasol, small bistratified) that transmit high-resolution visual signals to the brain. Fine-grained visual stimulation was used to identify the location, type and strength of the functional input of each cone photoreceptor to each ganglion cell. The populations of ON and OFF midget and parasol cells each sampled the complete population of long and middle wavelength sensitive cones. However, only OFF midget cells frequently received strong input from short wavelength sensitive cones. ON and OFF midget cells exhibited a small non-random tendency to selectively sample from either long or middle wavelength sensitive cones, to a degree not explained by clumping in the cone mosaic. These measurements reveal computations in a neural circuit at the elementary resolution of individual neurons.

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          Most cited references51

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          Chromatic mechanisms in lateral geniculate nucleus of macaque.

          This paper introduces a new technique for the analysis of the chromatic properties of neurones, and applies it to cells in the lateral geniculate nucleus (l.g.n.) of macaque. The method exploits the fact that for any cell that combines linearly the signals from cones there is a restricted set of lights to which it is equally sensitive, and whose members can be exchanged for one another without evoking a response. Stimuli are represented in a three-dimensional space defined by an axis along which only luminance varies, without change in chromaticity, a 'constant B' axis along which chromaticity varies without changing the excitation of blue-sensitive (B) cones, a 'constant R & G' axis along which chromaticity varies without change in the excitation of red-sensitive (R) or green-sensitive (G) cones. The orthogonal axes intersect at a white point. The isoluminant plane defined by the intersection of the 'constant B' and 'constant R & G' axes contains lights that vary only in chromaticity. In polar coordinates the constant B axis is assigned the azimuth 0-180 deg, and the constant R & G axis the azimuth 90-270 deg. Luminance is expressed as elevation above or below the isoluminant plane (-90 to +90 deg). For any cell that combines cone signals linearly, there is one plane in this space, passing through the white point, that contains all lights that can be exchanged silently. The position of this 'null plane' provides the 'signature' of the cell, and is specified by its azimuth (the direction in which it intersects the isoluminant plane of the stimulus space) and its elevation (its angle of inclination to the isoluminant plane). A colour television receiver was used to produce sinusoidal gratings whose chromaticity and luminance could be modulated along any vector passing through the white point in the space described. The spatial and temporal frequencies of modulation could be varied over a large range. When stimulated by patterns of low spatial and low temporal frequency, two groups of cells in the parvocellular laminae of the l.g.n. were distinguished by the locations of their null planes. The null planes of the larger group were narrowly distributed about an azimuth of 92.6 deg and more broadly about an elevation of 51.5 deg, which suggests that they receive opposed, but not equally balanced, inputs from only R and G cones. These we call R-G cells.(ABSTRACT TRUNCATED AT 400 WORDS)
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            The arrangement of the three cone classes in the living human eye.

            Human colour vision depends on three classes of receptor, the short- (S), medium- (M), and long- (L) wavelength-sensitive cones. These cone classes are interleaved in a single mosaic so that, at each point in the retina, only a single class of cone samples the retinal image. As a consequence, observers with normal trichromatic colour vision are necessarily colour blind on a local spatial scale. The limits this places on vision depend on the relative numbers and arrangement of cones. Although the topography of human S cones is known, the human L- and M-cone submosaics have resisted analysis. Adaptive optics, a technique used to overcome blur in ground-based telescopes, can also overcome blur in the eye, allowing the sharpest images ever taken of the living retina. Here we combine adaptive optics and retinal densitometry to obtain what are, to our knowledge, the first images of the arrangement of S, M and L cones in the living human eye. The proportion of L to M cones is strikingly different in two male subjects, each of whom has normal colour vision. The mosaics of both subjects have large patches in which either M or L cones are missing. This arrangement reduces the eye's ability to recover colour variations of high spatial frequency in the environment but may improve the recovery of luminance variations of high spatial frequency.
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              Dynamic predictive coding by the retina.

              Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                26 August 2010
                7 October 2010
                7 April 2011
                : 467
                : 7316
                : 673-677
                Affiliations
                [1 ] Systems Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA
                [2 ] Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA
                [3 ] Department of Physics and Astronomy, University of Glasgow, Glasgow, UK
                [4 ] Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 23 30-059, Krakow, Poland
                [5 ] Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY
                Author notes
                Contact: E.J. Chichilnisky, Systems Neurobiology, The Salk Institute, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA. ej@ 123456salk.edu , phone: (858) 453-4100 x1286
                [*]

                Equal contributions

                Article
                nihpa228287
                10.1038/nature09424
                2953734
                20930838
                52d0a1af-05ca-4cb3-94cc-8337900f7277

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Eye Institute : NEI
                Funded by: National Institute of Neurological Disorders and Stroke : NINDS
                Award ID: R01 EY013150-05 ||EY
                Funded by: National Eye Institute : NEI
                Funded by: National Institute of Neurological Disorders and Stroke : NINDS
                Award ID: F31 NS054519-01 ||NS
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