Dynamic coding of border-ownership in visual cortex

Layton, Oliver W.; Mingolla, Ennio; Yazdanbakhsh, Arash

doi:10.1167/12.13.8

Cited by 33 publications

(33 citation statements)

References 49 publications

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“…Although a fraction of V1 neurons show similar selectivity, they only do so with delays consistent with feedback from another cortical area (i.e., V2). This functionality may allow V2 neurons to perform figure/ground segmentation and to identify border ownership (Layton, Mingolla, & Yazdanbakhsh, 2012;Zhou, Friedman, & von der Heydt, 2000), perhaps in concert with higher cortical areas.…”

Section: Contextual Modulation Within V2 and V4mentioning

confidence: 99%

Contextual modulation and stimulus selectivity in extrastriate cortex

Krause

Pack

2014

Vision Research

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Contextual modulation is observed throughout the visual system, using techniques ranging from single-neuron recordings to behavioral experiments. Its role in generating feature selectivity within the retina and primary visual cortex has been extensively described in the literature. Here, we describe how similar computations can also elaborate feature selectivity in the extrastriate areas of both the dorsal and ventral streams of the primate visual system. We discuss recent work that makes use of normalization models to test specific roles for contextual modulation in visual cortex function. We suggest that contextual modulation renders neuronal populations more selective for naturalistic stimuli. Specifically, we discuss contextual modulation's role in processing optic flow in areas MT and MST and for representing naturally occurring curvature and contours in areas V4 and IT. We also describe how the circuitry that supports contextual modulation is robust to variations in overall input levels. Finally, we describe how this theory relates to other hypothesized roles for contextual modulation.

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Section: Contextual Modulation Within V2 and V4mentioning

confidence: 99%

Contextual modulation and stimulus selectivity in extrastriate cortex

Krause

Pack

2014

Vision Research

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“…Also, their model only provides a coarse picture of the timing of contour integration and border-ownership assignment across visual areas. Layton et al (2012) also proposed a model that performs border-ownership assignment. Their model introduces an additional neuron class (“R cells”) that implements competition between grouping cells of different RF sizes (similar to a model by Ardila et al (2012)), and the feedback to B cells is by means of shunting inhibition instead of the gain-modulation that we use in our model.…”

Section: Introductionmentioning

confidence: 99%

A recurrent neural model for proto-object based contour integration and figure-ground segregation

Niebur

2017

J Comput Neurosci

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Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects (“proto-objects”) based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al, 2000; Qiu et al, 2007; Chen et al, 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

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“…The parvocellular pathway of the proposed model on its own is similar to a number of existing models that reproduce important characteristics of B cells through feedback from units with larger receptive fields (Craft et al, 2007;Layton, Mingolla, & Yazdanbakhsh, 2012;Mihalas et al, 2011). The proposed model is the only one we are aware of that specifically addresses border-ownership in displays containing kinetic occlusion.…”

Section: Comparison With Existing Modelsmentioning

confidence: 84%

“…The objective of the model is to clarify how border-ownership signals emerge based on the known connectivity between visual areas, despite whether the figure and background are defined by luminance contrast or motion. The parvocellular pathway is based on a simplified version of a previous model that focuses on border-ownership of figures defined by luminance contrast (Layton, Mingolla, & Yazdanbakhsh, 2012). Simplifications are possible because the present article focuses on figure-ground segregation in random dot displays (e.g.…”

Section: Methodsmentioning

confidence: 99%

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A neural model of border-ownership from kinetic occlusion

Layton

Yazdanbakhsh

2015

Vision Research

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Camouflaged animals that have very similar textures to their surroundings are difficult to detect when stationary. However, when an animal moves, humans readily see a figure at a different depth than the background. How do humans perceive a figure breaking camouflage, even though the texture of the figure and its background may be statistically identical in luminance? We present a model that demonstrates how the primate visual system performs figure-ground segregation in extreme cases of breaking camouflage based on motion alone. Border-ownership signals develop as an emergent property in model V2 units whose receptive fields are nearby kinetically defined borders that separate the figure and background. Model simulations support border-ownership as a general mechanism by which the visual system performs figure-ground segregation, despite whether figure-ground boundaries are defined by luminance or motion contrast. The gradient of motion- and luminance-related border-ownership signals explains the perceived depth ordering of the foreground and background surfaces. Our model predicts that V2 neurons, which are sensitive to kinetic edges, are selective to border-ownership (magnocellular B cells). A distinct population of model V2 neurons is selective to border-ownership in figures defined by luminance contrast (parvocellular B cells). B cells in model V2 receive feedback from neurons in V4 and MT with larger receptive fields to bias border-ownership signals toward the figure. We predict that neurons in V4 and MT sensitive to kinetically defined figures play a crucial role in determining whether the foreground surface accretes, deletes, or produces a shearing motion with respect to the background.

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Dynamic coding of border-ownership in visual cortex

Cited by 33 publications

References 49 publications

Contextual modulation and stimulus selectivity in extrastriate cortex

Contextual modulation and stimulus selectivity in extrastriate cortex

A recurrent neural model for proto-object based contour integration and figure-ground segregation

A neural model of border-ownership from kinetic occlusion

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