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The gamma slideshow: object-based perceptual cycles in a model of the visual cortex.

Miconi T, Vanrullen R - Front Hum Neurosci (2010)

Bottom Line: We describe a simple model of V1 in which such perceptual cycles emerge automatically from the interaction between lateral excitatory connections (linking oriented cells falling along a continuous contour) and fast feedback inhibition (implementing competitive firing and gamma oscillations).Despite its extreme simplicity, the model spontaneously gives rise to perceptual cycles even when faced with natural images.The robustness of the system to parameter variation and to image complexity, together with the paucity of assumptions built in the model, support the hypothesis that perceptual cycles occur in natural vision.

View Article: PubMed Central - PubMed

Affiliation: Centre de Recherche Cerveau et Cognition, Université de Toulouse, Université Paul Sabatier Toulouse, France.

ABSTRACT
While recent studies have shed light on the mechanisms that generate gamma (>40 Hz) oscillations, the functional role of these oscillations is still debated. Here we suggest that the purported mechanism of gamma oscillations (feedback inhibition from local interneurons), coupled with lateral connections implementing "Gestalt" principles of object integration, naturally leads to a decomposition of the visual input into object-based "perceptual cycles," in which neuron populations representing different objects within the scene will tend to fire at successive cycles of the local gamma oscillation. We describe a simple model of V1 in which such perceptual cycles emerge automatically from the interaction between lateral excitatory connections (linking oriented cells falling along a continuous contour) and fast feedback inhibition (implementing competitive firing and gamma oscillations). Despite its extreme simplicity, the model spontaneously gives rise to perceptual cycles even when faced with natural images. The robustness of the system to parameter variation and to image complexity, together with the paucity of assumptions built in the model, support the hypothesis that perceptual cycles occur in natural vision.

No MeSH data available.


Application of the system to pictures of natural scenes. First column: original pictures. Second column: neurons with activity over 50% of the maximum, assigned by hand to different objects within the scene. Third column: Correlation between PSTH within and between objects, controlling for distance. Fourth column: rasterplots for example runs, with spikes colored accordingly to the object covering the neuron's receptive field center, with the same color assignment as in second column. See Materials and Methods for details.
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Figure 5: Application of the system to pictures of natural scenes. First column: original pictures. Second column: neurons with activity over 50% of the maximum, assigned by hand to different objects within the scene. Third column: Correlation between PSTH within and between objects, controlling for distance. Fourth column: rasterplots for example runs, with spikes colored accordingly to the object covering the neuron's receptive field center, with the same color assignment as in second column. See Materials and Methods for details.

Mentions: In order to provide a stronger challenge, we presented the model with a set of natural images, covering a range of different scene types (indoor, landscape, animals, etc.). These pictures were taken from the Corel database, cropped and resized to 96 × 96 pixels and converted into grayscale values (see Figure 5). The only modification of the model is that we raise the relative level of background excitation to 1 nA in order to dampen the larger dynamic range of the pictures. Figure 5 shows the resulting correlations for spiking activities between and within objects, as well as example rasterplots to illustrate the dynamics of the system. Despite the extreme simplicity of the model (in particular the absence of feedback, multi-scale processing, or special filters such as T/L detectors, etc.), the results shown in Figure 5 show that the system is still able to segregate between objects, ensuring that pixels corresponding to the same object will tend to fire together more often than pixels belonging to different objects (see also movies depicting the results of the system in Supplementary Material). While neurons not assigned to any object are not shown on the example rasterplots (since object-encoding neurons are the focus of our hypothesis), they simply fire in rhythm with the general oscillation imposed by the common inhibitory source. Unsurprisingly, the quantitative and qualitative (visual) results are clearly less pronounced than for simple pictures, indicating that the segregation is less reliable. Furthermore, results are variable between pictures. Nevertheless, the different objects are visibly segregated on visual inspection, which is numerically confirmed by the large difference between within-object and between-objects correlations in spiking activities for each image. This further confirms the robustness of the mechanism, and thus its intrinsic plausibility.


The gamma slideshow: object-based perceptual cycles in a model of the visual cortex.

Miconi T, Vanrullen R - Front Hum Neurosci (2010)

Application of the system to pictures of natural scenes. First column: original pictures. Second column: neurons with activity over 50% of the maximum, assigned by hand to different objects within the scene. Third column: Correlation between PSTH within and between objects, controlling for distance. Fourth column: rasterplots for example runs, with spikes colored accordingly to the object covering the neuron's receptive field center, with the same color assignment as in second column. See Materials and Methods for details.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992033&req=5

Figure 5: Application of the system to pictures of natural scenes. First column: original pictures. Second column: neurons with activity over 50% of the maximum, assigned by hand to different objects within the scene. Third column: Correlation between PSTH within and between objects, controlling for distance. Fourth column: rasterplots for example runs, with spikes colored accordingly to the object covering the neuron's receptive field center, with the same color assignment as in second column. See Materials and Methods for details.
Mentions: In order to provide a stronger challenge, we presented the model with a set of natural images, covering a range of different scene types (indoor, landscape, animals, etc.). These pictures were taken from the Corel database, cropped and resized to 96 × 96 pixels and converted into grayscale values (see Figure 5). The only modification of the model is that we raise the relative level of background excitation to 1 nA in order to dampen the larger dynamic range of the pictures. Figure 5 shows the resulting correlations for spiking activities between and within objects, as well as example rasterplots to illustrate the dynamics of the system. Despite the extreme simplicity of the model (in particular the absence of feedback, multi-scale processing, or special filters such as T/L detectors, etc.), the results shown in Figure 5 show that the system is still able to segregate between objects, ensuring that pixels corresponding to the same object will tend to fire together more often than pixels belonging to different objects (see also movies depicting the results of the system in Supplementary Material). While neurons not assigned to any object are not shown on the example rasterplots (since object-encoding neurons are the focus of our hypothesis), they simply fire in rhythm with the general oscillation imposed by the common inhibitory source. Unsurprisingly, the quantitative and qualitative (visual) results are clearly less pronounced than for simple pictures, indicating that the segregation is less reliable. Furthermore, results are variable between pictures. Nevertheless, the different objects are visibly segregated on visual inspection, which is numerically confirmed by the large difference between within-object and between-objects correlations in spiking activities for each image. This further confirms the robustness of the mechanism, and thus its intrinsic plausibility.

Bottom Line: We describe a simple model of V1 in which such perceptual cycles emerge automatically from the interaction between lateral excitatory connections (linking oriented cells falling along a continuous contour) and fast feedback inhibition (implementing competitive firing and gamma oscillations).Despite its extreme simplicity, the model spontaneously gives rise to perceptual cycles even when faced with natural images.The robustness of the system to parameter variation and to image complexity, together with the paucity of assumptions built in the model, support the hypothesis that perceptual cycles occur in natural vision.

View Article: PubMed Central - PubMed

Affiliation: Centre de Recherche Cerveau et Cognition, Université de Toulouse, Université Paul Sabatier Toulouse, France.

ABSTRACT
While recent studies have shed light on the mechanisms that generate gamma (>40 Hz) oscillations, the functional role of these oscillations is still debated. Here we suggest that the purported mechanism of gamma oscillations (feedback inhibition from local interneurons), coupled with lateral connections implementing "Gestalt" principles of object integration, naturally leads to a decomposition of the visual input into object-based "perceptual cycles," in which neuron populations representing different objects within the scene will tend to fire at successive cycles of the local gamma oscillation. We describe a simple model of V1 in which such perceptual cycles emerge automatically from the interaction between lateral excitatory connections (linking oriented cells falling along a continuous contour) and fast feedback inhibition (implementing competitive firing and gamma oscillations). Despite its extreme simplicity, the model spontaneously gives rise to perceptual cycles even when faced with natural images. The robustness of the system to parameter variation and to image complexity, together with the paucity of assumptions built in the model, support the hypothesis that perceptual cycles occur in natural vision.

No MeSH data available.