Chapter 12 Processing of second-order stimuli in the visual cortex

Baker, Curtis L.; Mareschal, Isabelle

doi:10.1016/s0079-6123(01)34013-x

Cited by 92 publications

(93 citation statements)

References 89 publications

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“…Although area 18 responses to interference patterns have previously been attributed to intracortical processing of the output of area 17, the present findings suggest that LGN Y-cells, the principle LGN input to area 18, may provide the nonlinear signal from which cor- has not been widely accepted. This is because cortical electrophysiology in both cats (Mareschal and Baker, 1998b) and monkeys (von der Heydt and Peterhans, 1989) as well as human psychophysics (Langley et al, 1996;Dakin and Mareschal, 2000) have suggested that the representation of non-Fourier features is constructed from the output of neurons with cortex-like tuning properties (Baker and Mareschal, 2001). The present findings, which demonstrate that the non-Fourier responses of LGN Y-cells are similar to those observed in cortex, may resolve this discrepancy.…”

Section: Discussionmentioning

confidence: 99%

“…This has led to the suggestion that the representation of non-Fourier features is synthesized cortically from the output of primary visual cortex (von der Heydt and Peterhans, 1989;Langley et al, 1996;Baker, 1998b, 1999;Baker and Mareschal, 2001). However, the degree to which Y-cells are selective for carrier orientation has not been studied.…”

Section: Orientation Selectivity Of the Linear And Nonlinear Responsementioning

confidence: 99%

“…Cortical electrophysiology in cats (Mareschal and Baker, 1998b) and monkeys (von der Heydt and Peterhans, 1989) as well as human psychophysics (Langley et al, 1996;Dakin and Mareschal, 2000) have suggested that the neural representation of non-Fourier features is constructed from an earlier cortex-like representation of the visual scene. Within the mammalian visual system, the prevailing model for the representation of non-Fourier features is thus a hierarchical one: the visual scene is represented in a dominantly linear manner through primary visual cortex after which nonFourier features are extracted via a nonlinear intracortical transformation (Baker and Mareschal, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Subcortical Representation of Non-Fourier Image Features

Rosenberg¹,

Husson²,

Issa³

2010

J. Neurosci.

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Section: Discussionmentioning

confidence: 99%

Section: Orientation Selectivity Of the Linear And Nonlinear Responsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Subcortical Representation of Non-Fourier Image Features

Rosenberg¹,

Husson²,

Issa³

2010

J. Neurosci.

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“…LN models of STRFs are conceptually intuitive and provide a compact description of simple cells and a common ground for comparison of different approaches. Secondary visual cortex is a more complex, intermediate-level processing stage with neurons selective for complex patterns (Hegde and Van Essen, 2000; Baker and Mareschal, 2001), thus providing a more demanding test of these different approaches.…”

Section: Introductionmentioning

confidence: 99%

Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness

Talebi

Baker

2012

J. Neurosci.

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An ultimate goal of visual neuroscience is to understand the neural encoding of complex, everyday scenes. Yet most of our knowledge of neuronal receptive fields has come from studies using simple artificial stimuli (e.g., bars, gratings) that may fail to reveal the full nature of a neuron's actual response properties. Our goal was to compare the utility of artificial and natural stimuli for estimating receptive field (RF) models. Using extracellular recordings from simple type cells in cat A18, we acquired responses to three types of broadband stimulus ensembles: two widely used artificial patterns (white noise and short bars), and natural images. We used a primary dataset to estimate the spatiotemporal receptive field (STRF) with two hold-back datasets for regularization and validation. STRFs were estimated using an iterative regression algorithm with regularization and subsequently fit with a zero-memory nonlinearity. Each RF model (STRF and zero-memory nonlinearity) was then used in simulations to predict responses to the same stimulus type used to estimate it, as well as to other broadband stimuli and sinewave gratings. White noise stimuli often elicited poor responses leading to noisy RF estimates, while short bars and natural image stimuli were more successful in driving A18 neurons and producing clear RF estimates with strong predictive ability. Natural image-derived RF models were the most robust at predicting responses to other broadband stimulus ensembles that were not used in their estimation and also provided good predictions of tuning curves for sinewave gratings.

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“…A model with two-stage processing has been proposed for the mechanism of texture segregation, where the rectified outputs of linear filters sensitive to luminance variation are detected by a second set of linear filters. The processing model has been verified by psychophysical and physiological experiments (e.g., Baker & Mareschal, 2001;Chubb & Sperling, 1988;Landy & Bergen, 1991).…”

Section: Scope and Purposementioning

confidence: 79%

Texture Segregation and Texture Transparency

Kawabe

Miura

2005

PSYCHOLOGIA -An International Journal of Psychological Sciences

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This paper reviews the literature on texture segregation and texture transparency, and proposes a new account of texture transparency based on the theory of texture segregation. To date, it has been suggested that the perception of transparency might stem from perceptual grouping or spacing effects between texture elements. Based on recent data, this paper argues that texture transparency should be explained by a Filter-Rectify-Filter mechanism, which underlies texture segregation; the integration of collinear texture edges and the separation of orthogonal texture edges at texture-defined junctions are a critical factor in causing texture transparency. Moreover, outstanding problems with the theory of texture transparency were discussed in terms of the nature of second-order junctions, the time-course of overlaid texture surfaces, and synergetic effects by combining several visual attributes such as orientation and spatial frequency.

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Chapter 12 Processing of second-order stimuli in the visual cortex

Cited by 92 publications

References 89 publications

Subcortical Representation of Non-Fourier Image Features

Subcortical Representation of Non-Fourier Image Features

Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness

Texture Segregation and Texture Transparency

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