Early Visual Cortex Assigns Border Ownership in Natural Scenes According to Image Context

Natural scenes contain a variety of visual cues that facilitate boundary perception (e.g., luminance, contrast, and texture). Here we explore whether single neurons in early visual cortex can process both contrast and texture cues. We recorded neural responses in cat A18 to both illusory contours formed by abutting gratings (ICs, texture-defined) and contrast-modulated gratings (CMs, contrast-defined). We found that if a neuron responded to one of the two stimuli, it also responded to the other. These neurons signaled similar contour orientation, spatial frequency, and movement direction of the two stimuli. A given neuron also exhibited similar selectivity for spatial frequency of the fine, stationary grating components (carriers) of the stimuli. These results suggest that the cue-invariance of early cortical neurons extends to different kinds of texture or contrast cues, and might arise from a common nonlinear mechanism.

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The critical reliance of early visual cortex on the fractal structure of natural scenes

Journal of Vision ◽

10.1167/19.10.123 ◽

2019 ◽

Vol 19 (10) ◽

pp. 123

Author(s):

Zoey J Isherwood ◽

Colin WG Clifford ◽

Mark M Schira ◽

Branka Spehar

Keyword(s):

Visual Cortex ◽

Fractal Structure ◽

Natural Scenes ◽

Early Visual Cortex

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Figure-ground organization in visual cortex for natural scenes

10.1101/053488 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jonathan R. Williford ◽

Rüdiger von der Heydt

Keyword(s):

Visual Cortex ◽

Temporal Cortex ◽

Stimulus Onset ◽

Natural Scenes ◽

Area V2 ◽

Response Onset ◽

Static Images ◽

Simple Figure ◽

Image Context ◽

Ground Organization

AbstractFigure-ground organization and border-ownership assignment are essential for understanding natural scenes. It has been shown that many neurons in the macaque visual cortex signal border-ownership in displays of simple geometric shapes such as squares, but how well these neurons resolve border-ownership in natural scenes is not known. We studied area V2 neurons in behaving macaques with static images of complex natural scenes. We found that about half of the neurons were border-ownership selective for contours in natural scenes and this selectivity originated from the image context. The border-ownership signals emerged within 70 ms after stimulus onset, only ~30 ms after response onset. A substantial fraction of neurons were highly consistent across scenes. Thus, the cortical mechanisms of figure-ground organization are fast and efficient even in images of complex natural scenes. Understanding how the brain performs this task so fast remains a challenge.Significance StatementHere we show, for the first time, that neurons in primate visual area V2 signal border-ownership for objects in complex natural scenes. Surprisingly, these signals appear as early as the border-ownership signals for simple figure displays. In fact, they emerge well before object selective activity appears in infero-temporal cortex, which rules out feedback from that region as an explanation. Thus, “objectness” is detected by extremely fast mechanisms that do not depend on feedback from the known object-recognition centers.

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Faculty Opinions recommendation of Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1007232.90607 ◽

2002 ◽

Author(s):

Shimon Ullman

Keyword(s):

Visual Cortex ◽

Neural Activity ◽

Higher Order ◽

Early Visual Cortex

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Faculty Opinions recommendation of Coding of natural scenes in primary visual cortex.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1012782.187756 ◽

2003 ◽

Author(s):

Bruno Olshausen

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Natural Scenes

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Faculty Opinions recommendation of Bottom-up dependent gating of frontal signals in early visual cortex.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1161778.623380 ◽

2009 ◽

Author(s):

Jitendra Sharma ◽

Hiroki Sugihara

Keyword(s):

Visual Cortex ◽

Bottom Up ◽

Early Visual Cortex

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Faculty Opinions recommendation of Confuse your illusion: feedback to early visual cortex contributes to perceptual completion.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717976773.793470558 ◽

2013 ◽

Author(s):

Chris Baker

Keyword(s):

Visual Cortex ◽

Perceptual Completion ◽

Early Visual Cortex

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Decoding the future from past experience: learning shapes predictions in early visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00753.2014 ◽

2015 ◽

Vol 113 (9) ◽

pp. 3159-3171 ◽

Cited By ~ 7

Author(s):

Caroline D. B. Luft ◽

Alan Meeson ◽

Andrew E. Welchman ◽

Zoe Kourtzi

Keyword(s):

Visual Cortex ◽

Large Scale ◽

Sequence Structure ◽

Neural Populations ◽

Early Visual Cortex ◽

Temporal Structures ◽

Future Events ◽

Sensory Predictions ◽

Actual Stimulus ◽

The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

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