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A simple retinal mechanism contributes to perceptual interactions between rod- and cone-mediated responses in primates.

Grimes WN, Graves LR, Summers MT, Rieke F - Elife (2015)

Bottom Line: Because responses of retinal ganglion cells, the output cells of the retina, depend on signals from both rod and cone photoreceptors, interactions occurring in retinal circuits provide an opportunity to link the mechanistic operation of parallel pathways and perception.Here we show that rod- and cone-mediated responses interact nonlinearly to control the responses of primate retinal ganglion cells; these nonlinear interactions, surprisingly, were asymmetric, with rod responses strongly suppressing subsequent cone responses but not vice-versa.Human psychophysical experiments revealed a similar perceptual asymmetry.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology and Biophysics, Howard Hughes Medical Institute, University of Washington, Seattle, United States.

ABSTRACT
Visual perception across a broad range of light levels is shaped by interactions between rod- and cone-mediated signals. Because responses of retinal ganglion cells, the output cells of the retina, depend on signals from both rod and cone photoreceptors, interactions occurring in retinal circuits provide an opportunity to link the mechanistic operation of parallel pathways and perception. Here we show that rod- and cone-mediated responses interact nonlinearly to control the responses of primate retinal ganglion cells; these nonlinear interactions, surprisingly, were asymmetric, with rod responses strongly suppressing subsequent cone responses but not vice-versa. Human psychophysical experiments revealed a similar perceptual asymmetry. Nonlinear interactions in the retinal output cells were well-predicted by linear summation of kinetically-distinct rod- and cone-mediated signals followed by a synaptic nonlinearity. These experiments thus reveal how a simple mechanism controlling interactions between parallel pathways shapes circuit output and perception.

No MeSH data available.


Related in: MedlinePlus

Linear summation followed by a rectifying nonlinearity can account for rod-cone interactions.(A) Linear-nonlinear (LN) model construction. A time-varying rod- or cone-preferring stimulus and the resulting RGC excitatory synaptic inputs were used to derive the linear filter and static nonlinearity that relate the stimulus to the response. (B) Normalized linear filters for rod and cone stimuli. (C) Nonlinearities for rod and cone stimuli. (D) Rod-cone interactions were predicted by summing scaled and temporally-offset (i.e., 0.2 s) rod- and cone-preferring filters and passing the result through a common nonlinearity (see ‘Materials and methods’). (E) Predicted rod → cone (left) and cone → rod (right) interactions. (F) Measured rod → cone and cone → rod interactions. (G) Population data comparing predictions from the LN models to experimental observations. Each point represents a single cell in which LN model components and rod-cone interactions were measured. All recordings from whole mount retina.DOI:http://dx.doi.org/10.7554/eLife.08033.011
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fig4: Linear summation followed by a rectifying nonlinearity can account for rod-cone interactions.(A) Linear-nonlinear (LN) model construction. A time-varying rod- or cone-preferring stimulus and the resulting RGC excitatory synaptic inputs were used to derive the linear filter and static nonlinearity that relate the stimulus to the response. (B) Normalized linear filters for rod and cone stimuli. (C) Nonlinearities for rod and cone stimuli. (D) Rod-cone interactions were predicted by summing scaled and temporally-offset (i.e., 0.2 s) rod- and cone-preferring filters and passing the result through a common nonlinearity (see ‘Materials and methods’). (E) Predicted rod → cone (left) and cone → rod (right) interactions. (F) Measured rod → cone and cone → rod interactions. (G) Population data comparing predictions from the LN models to experimental observations. Each point represents a single cell in which LN model components and rod-cone interactions were measured. All recordings from whole mount retina.DOI:http://dx.doi.org/10.7554/eLife.08033.011

Mentions: Can linear summation followed by a synaptic nonlinearity quantitatively account for the measured rod → cone and cone → rod interactions? To answer this question, we characterized the relation between a time-varying stimulus (input) and the excitatory ganglion cell response (output) using a linear-nonlinear cascade model (LN model; see ‘Materials and methods’), and then used this description to generate a parameter-free model for rod → cone and cone → rod flash interactions (Figure 4B–E). We derived the LN model components (i.e., the linear filter and static nonlinearity) for both rod and cone inputs using gaussian noise stimuli. Linear filters for rod inputs were slower and more biphasic than those for cone inputs (Figure 4B), consistent with the differences observed in ON cone bipolar voltage responses (Figure 3A). The nonlinearities derived from rod and cone inputs were similar (Figure 4C), consistent with a location in a shared element in the rod and cone circuits.10.7554/eLife.08033.011Figure 4.Linear summation followed by a rectifying nonlinearity can account for rod-cone interactions.


A simple retinal mechanism contributes to perceptual interactions between rod- and cone-mediated responses in primates.

Grimes WN, Graves LR, Summers MT, Rieke F - Elife (2015)

Linear summation followed by a rectifying nonlinearity can account for rod-cone interactions.(A) Linear-nonlinear (LN) model construction. A time-varying rod- or cone-preferring stimulus and the resulting RGC excitatory synaptic inputs were used to derive the linear filter and static nonlinearity that relate the stimulus to the response. (B) Normalized linear filters for rod and cone stimuli. (C) Nonlinearities for rod and cone stimuli. (D) Rod-cone interactions were predicted by summing scaled and temporally-offset (i.e., 0.2 s) rod- and cone-preferring filters and passing the result through a common nonlinearity (see ‘Materials and methods’). (E) Predicted rod → cone (left) and cone → rod (right) interactions. (F) Measured rod → cone and cone → rod interactions. (G) Population data comparing predictions from the LN models to experimental observations. Each point represents a single cell in which LN model components and rod-cone interactions were measured. All recordings from whole mount retina.DOI:http://dx.doi.org/10.7554/eLife.08033.011
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Related In: Results  -  Collection

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Show All Figures
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fig4: Linear summation followed by a rectifying nonlinearity can account for rod-cone interactions.(A) Linear-nonlinear (LN) model construction. A time-varying rod- or cone-preferring stimulus and the resulting RGC excitatory synaptic inputs were used to derive the linear filter and static nonlinearity that relate the stimulus to the response. (B) Normalized linear filters for rod and cone stimuli. (C) Nonlinearities for rod and cone stimuli. (D) Rod-cone interactions were predicted by summing scaled and temporally-offset (i.e., 0.2 s) rod- and cone-preferring filters and passing the result through a common nonlinearity (see ‘Materials and methods’). (E) Predicted rod → cone (left) and cone → rod (right) interactions. (F) Measured rod → cone and cone → rod interactions. (G) Population data comparing predictions from the LN models to experimental observations. Each point represents a single cell in which LN model components and rod-cone interactions were measured. All recordings from whole mount retina.DOI:http://dx.doi.org/10.7554/eLife.08033.011
Mentions: Can linear summation followed by a synaptic nonlinearity quantitatively account for the measured rod → cone and cone → rod interactions? To answer this question, we characterized the relation between a time-varying stimulus (input) and the excitatory ganglion cell response (output) using a linear-nonlinear cascade model (LN model; see ‘Materials and methods’), and then used this description to generate a parameter-free model for rod → cone and cone → rod flash interactions (Figure 4B–E). We derived the LN model components (i.e., the linear filter and static nonlinearity) for both rod and cone inputs using gaussian noise stimuli. Linear filters for rod inputs were slower and more biphasic than those for cone inputs (Figure 4B), consistent with the differences observed in ON cone bipolar voltage responses (Figure 3A). The nonlinearities derived from rod and cone inputs were similar (Figure 4C), consistent with a location in a shared element in the rod and cone circuits.10.7554/eLife.08033.011Figure 4.Linear summation followed by a rectifying nonlinearity can account for rod-cone interactions.

Bottom Line: Because responses of retinal ganglion cells, the output cells of the retina, depend on signals from both rod and cone photoreceptors, interactions occurring in retinal circuits provide an opportunity to link the mechanistic operation of parallel pathways and perception.Here we show that rod- and cone-mediated responses interact nonlinearly to control the responses of primate retinal ganglion cells; these nonlinear interactions, surprisingly, were asymmetric, with rod responses strongly suppressing subsequent cone responses but not vice-versa.Human psychophysical experiments revealed a similar perceptual asymmetry.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology and Biophysics, Howard Hughes Medical Institute, University of Washington, Seattle, United States.

ABSTRACT
Visual perception across a broad range of light levels is shaped by interactions between rod- and cone-mediated signals. Because responses of retinal ganglion cells, the output cells of the retina, depend on signals from both rod and cone photoreceptors, interactions occurring in retinal circuits provide an opportunity to link the mechanistic operation of parallel pathways and perception. Here we show that rod- and cone-mediated responses interact nonlinearly to control the responses of primate retinal ganglion cells; these nonlinear interactions, surprisingly, were asymmetric, with rod responses strongly suppressing subsequent cone responses but not vice-versa. Human psychophysical experiments revealed a similar perceptual asymmetry. Nonlinear interactions in the retinal output cells were well-predicted by linear summation of kinetically-distinct rod- and cone-mediated signals followed by a synaptic nonlinearity. These experiments thus reveal how a simple mechanism controlling interactions between parallel pathways shapes circuit output and perception.

No MeSH data available.


Related in: MedlinePlus