Blue-Yellow Signals Are Enhanced by Spatiotemporal Luminance Contrast in Macaque V1

Horwitz, Gregory D.; Chichilnisky, E. J.; Albright, Thomas D.

doi:10.1152/jn.00743.2004

Cited by 59 publications

(53 citation statements)

References 59 publications

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“…Only significant center and surround responses (Ͼ2.5 SDs of the background) were analyzed. with evidence showing nonlinear luminance responses in blueyellow neurons (Horwitz et al, 2005). The S center response in the S-ON cells was often large (Fig.…”

Section: Spatiotemporal Maps Of Blue-yellow Cellsmentioning

confidence: 89%

“…If the cortex were entirely linear in the way it combined inputs from the two states, it would be superfluous to explicitly distinguish them. However, there is evidence that V1 neurons, especially blue-yellow cells (Horwitz et al, 2005), do not sum signals linearly; moreover, psychophysical research indicates that sensitivity is not equal for light increments and light decrements (see Results, Responses to light increments and decrements).…”

Section: Push-pull Responses Of Cone-opponent Neuronsmentioning

confidence: 96%

“…Achromatic responses of color-sensitive neurons have been found by others (Conway, 2001;Johnson et al, 2004;Horwitz et al, 2005). Johnson et al (2001) have used the ratio of the response to equiluminant color and luminance as a means of categorizing neurons.…”

Section: Achromatic Responses Of Cone-opponent Neuronsmentioning

confidence: 99%

“…Luminance responses show that the cone inputs to any given cell may not be balanced, although as a population, at least for the red-green cells, they are. Luminance responses may enhance chromatic signals (Horwitz et al, 2005) and contribute to the binding of form and color (Billock and Tsou, 2004).…”

Section: Wiring Of Double-opponent Cellsmentioning

confidence: 99%

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Spatial and Temporal Properties of Cone Signals in Alert Macaque Primary Visual Cortex

Conway¹,

Livingstone²

2006

J. Neurosci.

140

185

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7°) than spatial-opponent cell centers (ϳ1°). We found that red-green cells received S-cone input, which aligned with M input, and, unlike blue-yellow cells, red-green cells gave push-pull responses: receptivefield centers of red-ON cells were excited by both L increments (bright red) and M decrements (dark red) and were suppressed by both L decrements (dark green) and M increments (bright green). Excitatory responses to decrements were slightly larger than to increments, which may account for the lower detection and discrimination thresholds of decrements shown psychophysically. By virtue of their specialized receptive fields, the neurons described here spatially transform the cone signals and represent the first stage in the visual system at which spatially opponent color calculations are made.

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Section: Spatiotemporal Maps Of Blue-yellow Cellsmentioning

confidence: 89%

Section: Push-pull Responses Of Cone-opponent Neuronsmentioning

confidence: 96%

Section: Achromatic Responses Of Cone-opponent Neuronsmentioning

confidence: 99%

Section: Wiring Of Double-opponent Cellsmentioning

confidence: 99%

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Spatial and Temporal Properties of Cone Signals in Alert Macaque Primary Visual Cortex

Conway¹,

Livingstone²

2006

J. Neurosci.

140

185

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“…In the simplest approximation, the feature is simply the spike-triggered average (STA). Covariance methods (de Ruyter van Steveninck and Bialek, 1988;Brenner et al, 2000a) Touryan et al, 2002;Fairhall et al, 2003;Petersen and Diamond, 2003;Horwitz et al, 2004;Rust et al, 2005). Although in studies of sensory responses to external stimuli it is difficult to determine the origins of neural feature selectivity in terms of biophysics or circuitry, here the system is isolated to a single neuron with current input.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Time Coding in the Auditory Brainstem

Slee¹,

Higgs²,

Fairhall³

et al. 2005

J. Neurosci.

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Avian nucleus magnocellularis (NM) spikes provide a temporal code representing sound arrival times to downstream neurons that compute sound source location. NM cells act as high-pass filters by responding only to discrete synaptic events while ignoring temporally summed EPSPs. This high degree of input selectivity insures that each output spike from NM unambiguously represents inputs that contain precise temporal information. However, we lack a quantitative description of the computation performed by NM cells. A powerful model for predicting output firing rate given an arbitrary current input is given by a linear/nonlinear cascade: the stimulus is compared with a known relevant feature by linear filtering, and based on that comparison, a nonlinear function predicts the firing response. Spike-triggered covariance analysis allows us to determine a generalization of this model in which firing depends on more than one spike-triggering feature or stimulus dimension. We found two current features relevant for NM spike generation; the most important simply smooths the current on short time scales, whereas the second confers sensitivity to rapid changes. A model based on these two features captured more mutual information between current and spikes than a model based on a single feature. We used this analysis to characterize the changes in the computation brought about by pharmacological manipulation of the biophysical properties of the neurons. Blockage of low-threshold voltage-gated potassium channels selectively eliminated the requirement for the second stimulus feature, generalizing our understanding of input selectivity by NM cells. This study demonstrates the power of covariance analysis for investigating single neuron computation.

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Color Vision

Webster

2018

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

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Advances in our understanding of color vision are proceeding on many fronts. These include analyses of the interplay of light and materials in natural scenes, to the genetic, neural, and cognitive processes underlying color sensitivity and percepts. The basic model for color vision, where the light spectrum is first sampled by receptors and then represented in opponent mechanisms, remains a cornerstone of color theory. However, the ways in which these processes are manifest and operate are surprisingly varied and still poorly understood. New developments continue to reveal that color vision involves highly flexible coding schemes that support sophisticated perceptual inferences. Characterizing these processes is providing fundamental insights not only into our experience of color, but into perception and neural coding generally.

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Blue-Yellow Signals Are Enhanced by Spatiotemporal Luminance Contrast in Macaque V1

Cited by 59 publications

References 59 publications

Spatial and Temporal Properties of Cone Signals in Alert Macaque Primary Visual Cortex

Spatial and Temporal Properties of Cone Signals in Alert Macaque Primary Visual Cortex

Two-Dimensional Time Coding in the Auditory Brainstem

Color Vision

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