Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2018 Nov:67:102-117.
doi: 10.1016/j.preteyeres.2018.06.003. Epub 2018 Jun 23.

The dynamic receptive fields of retinal ganglion cells

Sophia Wienbar  1 , Gregory W Schwartz  2
Affiliations
Review

The dynamic receptive fields of retinal ganglion cells

Sophia Wienbar et al. Prog Retin Eye Res. 2018 Nov.

Abstract

Retinal ganglion cells (RGCs) were one of the first classes of sensory neurons to be described in terms of a receptive field (RF). Over the last six decades, our understanding of the diversity of RGC types and the nuances of their response properties has grown exponentially. We will review the current understanding of RGC RFs mostly from studies in mammals, but including work from other vertebrates as well. We will argue for a new paradigm that embraces the fluidity of RGC RFs with an eye toward the neuroethology of vision. Specifically, we will focus on (1) different methods for measuring RGC RFs, (2) RF models, (3) feature selectivity and the distinction between fluid and stable RF properties, and (4) ideas about the future of understanding RGC RFs.

PubMed Disclaimer

Figures

Figure 1
Figure 1. Stimuli used for measuring RGC RFs
Figure 2
Figure 2. Estimated RF center size can depend on surround strength
(A) Schematic of the sum of a fixed RF center with either a weak or a strong surround. (B) A model of normalized response (integral of RF) as a function of spot size for 3 different strengths of the surround. The RF center size is fixed. This is the response one would measure with the spots-of-varying-size technique. Arrowheads indicate the spot size giving the peak response, often used as a measure of the RF center size. (C) Relationship between the standard deviation of the RF center Gaussian and the estimated RF size from the peak response for 3 different surround strengths as in (B).
Figure 3
Figure 3. Measurement of RF center and surround strength vary with the spatial resolution of a spatiotemporal white noise stimulus
(A) Example white noise stimuli at 3 spatial resolutions along with the RF center and RF surround sampled at each resolution. The final 2 columns show the RF center and surround measurements again, but scaled by their relative strength based on the contrast of the stimulus within the center and surround, respectively. Contrast scale corresponds to these last 2 columns. (B) The relationship between the pixel size of the white noise stimulus and the contrast elicited in the RF center and RF surround.
Figure 4
Figure 4. Schematics of the classes of RGC RF models
(A–C) Spatially linear models. (D–F) Spatially nonlinear models.
Figure 5
Figure 5. Methods for estimating subunit locations
(A) In the anatomical method, a RGC cell fill (blue) is combined with a marker of synapses (green) and a stain for a particular bipolar cell type (magenta). A model estimates the number of synapses from each bipolar cell based on the dendritic morphology of the RGC. Adapted from (Gregory W. Schwartz et al., 2012). (B) The single cone stimulation method presents small spots of light aligned to the locations of cones. By presenting pairs of spots and measuring whether the responses combine linearly or nonlinearly in the RGCs, the experimenters were able to infer the locations of RF subunits. Adapted from (Freeman et al., 2015). (C) The non-negative matrix factorization technique is an analytical method that can be applied to data from spatiotemporal white noise experiments (left). The panel at the rights shows the linear RF (gray) and the corresponding subunit RFs (red) estimated from a multi-electrode-array recording in salamander retina. Adapted from (Liu et al., 2017).
Figure 6
Figure 6. Polarity switches in RGCs with stimulus conditions
(A) Spike rasters from 2 RGCs responding to ON and OFF contrast steps across 5 log units of luminance. Polarity switches are indicated by shading for ON (yellow), ON-OFF (green) and OFF (blue) polarity. Adapted from (Pearson and Kerschensteiner, 2015). (B) The firing rate of a RGC that becomes ON-OFF in a limited luminance range. Adapted from (Tikidji-Hamburyan et al., 2015). (C) The response polarity of JAM-B RGCs depends on stimulus size. Adapted from (Kim et al., 2008).
Figure 7
Figure 7. RGCs respond with different kinetics to small and large spots of light
Four examples of RGC light responses to a spot of light (darkness to 200 isomerizations per rod per second) presented for 1 sec. Top traces show responses to spots covering only the RF center (120 μm for the F-miniON and 200 μm for the oher cells. Bottom traces show responses from the same four cells to a full-field spot (1200 μm) covering the RF center and surround.
Figure 8
Figure 8. Linear verses nonlinear spatial integration in RGCs can depend on luminance
(A) Example of the same ON-alpha responding to a contrast-reversing grating in scotopic and mesopic luminance. This is the same stimulus originally used to classify linear (X) vs. nonlinear (Y) RGCs (Enroth-Cugell and Robson, 1966). (B) A schematic of how a change in rectification at bipolar cell output synapses can account for a change in spatial integration in a RGC. Figure adapted from (William N. Grimes et al., 2014).
Figure 9
Figure 9. A subset of retinorecipient areas of the brain
Well studied brain regions are in color and listed with their known RGC inputs and their behavioral function. Less well understood regions are shown shaded in gray. Abbreviations: AHN: Anterior Hypothalamic Nucleus, APN: Anterior Pretectal Nucleus, IGL: Intergeniculate Leaflet, LGN: Lateral Geniculate Nucleus, LHA: Lateral Hypothalamic Area, MTN: Medial Terminal Nucleus, NOT/DTN: Nucleus of the Optic Tract/Dorsal Tegmental Nucleus, OPN: Olivary Pretectal Nucleus, PPN: Pedunculopontine Nucleus, RCH: Retrochiasmatic Area, SC: Superior Colliculus, SCN: Suprachiasmatic Nucleus.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ackert JM, Wu SH, Lee JC, Abrams J, Hu EH, Perlman I, Bloomfield SA. Light-induced changes in spike synchronization between coupled ON direction selective ganglion cells in the mammalian retina. J Neurosci. 2006;26:4206��4215. doi: 10.1523/JNEUROSCI.0496-06.2006. - DOI - PMC - PubMed
    1. Antinucci P, Suleyman O, Monfries C, Hindges R. Neural Mechanisms Generating Orientation Selectivity in the Retina. Curr Biol. 2016 doi: 10.1016/j.cub.2016.05.035. - DOI - PMC - PubMed
    1. Atick Redlich. Towards a theory of early visual processing. Neural Comput. 1990;2:308–320.
    1. Attwell D, Wilson M, Wu SM. A quantitative analysis of interactions between photoreceptors in the salamander (Ambystoma) retina. J Physiol. 1984;352:703–737. - PMC - PubMed
    1. Baccus SA, Meister M. Fast and Slow Contrast Adaptation in Retinal Circuitry. Neuron. 2002;36:909–919. - PubMed

Publication types

MeSH terms