Biomedical subjects

Rüdiger von der Heydt

Publications and source records attributed to Rüdiger von der Heydt.

3 recordsLinked to original sources

PubMed2005

Figure and ground in the visual cortex: v2 combines stereoscopic cues with gestalt rules.

Figure-ground organization is a process by which the visual system identifies some image regions as foreground and others as background, inferring 3D layout from 2D displays. A recent study reported that edge responses of neurons in area V2 are selective for side-of-figure, suggesting that figure-ground organization is encoded in the contour signals (border ownership coding). Here, we show that area V2 combines two strategies of computation, one that exploits binocular stereoscopic information for the definition of local depth order, and another that exploits the global configuration of contours (Gestalt factors). These are combined in single neurons so that the "near" side of the preferred 3D edge generally coincides with the preferred side-of-figure in 2D displays. Thus, area V2 represents the borders of 2D figures as edges of surfaces, as if the figures were objects in 3D space. Even in 3D displays, Gestalt factors influence the responses and can enhance or null the stereoscopic depth information.

Algorithms↗

PubMed2005

Border-ownership-dependent tilt aftereffect.

Single-cell recordings from macaque visual cortex have shown orientation-selective neurons in area in V2 code for border ownership [J. Neurosci. 20, 6594 (2000)]: Each piece of contrast border is represented by two pools of neurons whose relative firing rate indicates the side of border ownership. Here we show that the human visual cortex uses a similar coding scheme by demonstrating a border-ownership-contingent tilt aftereffect. The aftereffect was specific for the adapted location, indicating that the adapted neurons have small receptive fields. We conclude that figure-ground organization is represented by border-ownership-selective neurons at early stages in the human visual cortex.

Adaptation, Ocular↗

PubMed2003

The coding of uniform colour figures in monkey visual cortex.

Psychophysical studies indicate that perception of the colour and brightness of a surface depends on neural signals evoked by the borders of the surface rather than its interior. The visual cortex emphasizes contrast borders, but it is unclear whether colour surface signals also exist, whether colour border signals are orientation selective or mainly non-oriented, and whether cortical processing tends to separate colour and form information. To address these questions we examined the representation of uniform colour figures by recording single neuron activity from areas V1 and V2 in alert macaque monkeys during behaviourally induced fixation. Three aspects of coding were quantified: colour, orientation and edge selectivity. The occurrence of colour selectivity was not correlated with orientation or edge selectivity. The fraction of colour-selective cells was the same (64 % in layers 2 and 3 of V1, 45 % in V2) for oriented and non-oriented cells, and for edge-selective and surface-responsive cells. Oriented cells were often highly selective in colour space, and about 40 % of them were selective for edge polarity or border ownership. Thus, contrary to the idea of feature maps, colour, orientation and edge polarity are multiplexed in cortical signals. The results from V2 were similar to those from upper-layer V1, indicating that cortical processing does not strive to separate form and colour information. Oriented cells were five times more frequent than non-oriented cells. Thus, the vast majority of colour-coded cells are orientation tuned. Based on response profiles across a 4 deg square figure, and the relative frequency of oriented and non-oriented cells, we estimate that the cortical colour signal is 5-6 times stronger for the edges than for the surface of the figure. The frequency of oriented colour cells and their ability to code edge polarity indicate that these cells play a major role in the representation of surface colour.

Algorithms↗