Measurements of neuronal color tuning: Procedures, pitfalls, and alternatives

Weller, J. Patrick; Horwitz, Gregory D.

doi:10.1016/j.visres.2017.08.005

Cited by 9 publications

(10 citation statements)

References 53 publications

(62 reference statements)

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“…Together, the color and spatial weighting functions captured 96.7±5.0% (mean±SD) of the variance in the weighted STAs. The color weighting function was converted to cone weights that are assumed to act on cone contrast signals (Weller and Horwitz, 2018). The spatial weighting function of every cell consisted of one positive and one negative weight, because only neurons with spatially opponent RFs were recorded.…”

Section: Methodsmentioning

confidence: 99%

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Coding of chromatic spatial contrast by macaque V1 neurons

Horwitz

2021

Preprint

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Color perception relies on spatial comparisons of chromatic signals, but how the brain performs these comparisons is poorly understood. Here, we show that many V1 neurons compare signals across their receptive fields (RF) using a common principle. We estimated the spatial-chromatic RFs of each neuron, and then measured neural selectivity to customized colored edges that were sampled using a novel closed-loop technique. We found that many double-opponent (DO) cells, which have spatially and chromatically opponent RFs, responded to chromatic contrast as a weighted sum, akin to how simple cells respond to luminance contrast. Other neurons integrated chromatic signals non-linearly, confirming that linear signal integration is not an obligate property of V1 neurons. The functional similarity of DO and simple cells suggests a common underlying neural circuitry, promotes the construction of image-computable models for full-color image representation, and sheds new light on V1 complex cells.

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Section: Methodsmentioning

confidence: 99%

“…Cone weights are interpretable only under a linear model of signal combination across cone types (Weller and Horwitz, 2018). One way to test this assumption is by analysis of spike-triggered covariance.…”

Section: Spike-triggered Covariance Analysismentioning

confidence: 99%

Coding of chromatic spatial contrast by macaque V1 neurons

Horwitz

2021

Preprint

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“…The color weighting function, which quantifies neuronal sensitivity to modulations of the red, green, and blue phosphors of the display, was converted to cone weights that are assumed to act on cone contrast signals (Weller, 2018 & Shapley, 2004). We analyzed only those cells that were spatially opponent (see Cell Screening).…”

Section: Cone Weights and Spatial Rfmentioning

confidence: 99%

Spatial receptive field structure of double-opponent cells in macaque V1

Horwitz

2020

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Double-opponent (DO) cells are well-suited to contribute to the spatial processing of color due to their spatially opponent and cone-opponent receptive fields (RFs).However, the representation of visual images by DO cells in monkey primary visual cortex is unclear because the spatial RF structure of DO cells has not been fully characterized. Early reports suggested that DO cells have center-surround RFs, but more recent studies have shown that some DO cells are orientation-tuned. To characterize the RFs of DO cells, we mapped them in awake fixating macaques, fit them with parametric models, and compared them to the benchmark of orientationtuned simple cells. Neurons were stimulated with colorful, dynamic white noise patterns.Spike-triggered averaging was used to classify each neuron as simple, DO, or neither and to measure its spatial RF. The spatial RF of each neuron was fitted with a Gabor model and a Difference of Gaussians (DoG) model. The Gabor model provided the more accurate description, a result that is incompatible with a center-surround RF organization. The superiority of the Gabor fits was slightly more decisive for simple than for DO cells. A modified (non-concentric) DoG model performed nearly as well as the Gabor model for DO cells.

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“…Studies focused on developing quantitative parametric models of the chromatic response properties of neurons in primary visual cortex of primates (area V1) have not yet converged on a widely-accepted model (Johnson, Hawken, & Shapley, 2004;Solomon & Lennie, 2005;Tailby, Solomon, Dhruv, & Lennie, 2008;Horwitz & Hass, 2012;Weller & Horwitz, 2018). In part, this is due to the considerable heterogeneity of chromatic response properties found across individual cortical neurons (Gegenfurtner, 2001;Lennie & Movshon, 2005;Solomon & Lennie, 2007;Shapley & Hawken, 2011;Horwitz, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Quadratic Model Captures the Human V1 Response to Variations in Chromatic Direction and Contrast

Barnett

Aguirre

Brainard

2020

Preprint

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An important goal for vision science is to develop quantitative models for the representation of visual signals at post-receptoral sites. To this end, we develop the quadratic color model (QCM) and examine its ability to account for the BOLD fMRI response in human V1 to spatially-uniform temporal chromatic modulations, systematically varying in their chromatic directions and contrasts. We find that the QCM explains the same, cross-validated variance as a conventional GLM, with far fewer free parameters. The QCM generalizes to allow prediction of V1 responses to a large range of modulations. We replicated the results for each subject and find good agreement across both replications and subjects. We find that within the LM cone contrast plane, V1 is most sensitive to L-M contrast modulations and least sensitive to L+M contrast modulations. Within V1, we observe little to no change in chromatic sensitivity as a function of eccentricity.

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Measurements of neuronal color tuning: Procedures, pitfalls, and alternatives

Cited by 9 publications

References 53 publications

Coding of chromatic spatial contrast by macaque V1 neurons

Coding of chromatic spatial contrast by macaque V1 neurons

Spatial receptive field structure of double-opponent cells in macaque V1

A Quadratic Model Captures the Human V1 Response to Variations in Chromatic Direction and Contrast

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