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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.


Related in: MedlinePlus

Robustness of system behavior to parameter variation. Correlation between the time-binned PSTH of equally-spaced groups of neurons responding to the same object (blue crosses) and to different objects (red circles), when certain simulation parameters vary while all others remain fixed at their default values. The varying parameters are: (A) strength of EPSP carried by lateral connections; (B) peak amplitude of feedback inhibition; (C) delay between the first spike and the arrival of feedback inhibition; (D) peak amplitude of the after-hyperpolarization potential. All values are averages of 50 runs; error bars indicate s.e.m. Down arrow (▼) indicates default value. The expected behavior (positive within-object correlation, negative between-objects correlation) emerges over a wide range of parameters. Significant differences between within-object and between-objects correlations occur for an even wider range of parameters. However the behavior breaks down in the absence of lateral connections (top left) or feedback inhibition (top right), confirming that these are the crucial requirements for the emergence of perceptual cycles.
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Figure 4: Robustness of system behavior to parameter variation. Correlation between the time-binned PSTH of equally-spaced groups of neurons responding to the same object (blue crosses) and to different objects (red circles), when certain simulation parameters vary while all others remain fixed at their default values. The varying parameters are: (A) strength of EPSP carried by lateral connections; (B) peak amplitude of feedback inhibition; (C) delay between the first spike and the arrival of feedback inhibition; (D) peak amplitude of the after-hyperpolarization potential. All values are averages of 50 runs; error bars indicate s.e.m. Down arrow (▼) indicates default value. The expected behavior (positive within-object correlation, negative between-objects correlation) emerges over a wide range of parameters. Significant differences between within-object and between-objects correlations occur for an even wider range of parameters. However the behavior breaks down in the absence of lateral connections (top left) or feedback inhibition (top right), confirming that these are the crucial requirements for the emergence of perceptual cycles.

Mentions: How robust are these results with regard to the choice of parameters? Figure 4 shows the results of the system when letting certain parameters vary around the default values (keeping all other parameters equal). These figures indicate that cycling behavior occurs over a sizeable range of parameter values. This suggests that the emergence of perceptual cycles is a robust phenomenon, as opposed to a fragile result highly dependent on favorable parameters. In addition, we note that perceptual cycles are eliminated when either lateral connections or feedback inhibition tend toward zero: in both cases, correlations in firing become positive for all neurons, regardless of which object they represent. This confirms that these two mechanisms (feedback inhibition enforcing competitive firing and lateral connections implementing object integration) are the two crucial requirements for the emergence of perceptual cycles in our system.


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

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

Robustness of system behavior to parameter variation. Correlation between the time-binned PSTH of equally-spaced groups of neurons responding to the same object (blue crosses) and to different objects (red circles), when certain simulation parameters vary while all others remain fixed at their default values. The varying parameters are: (A) strength of EPSP carried by lateral connections; (B) peak amplitude of feedback inhibition; (C) delay between the first spike and the arrival of feedback inhibition; (D) peak amplitude of the after-hyperpolarization potential. All values are averages of 50 runs; error bars indicate s.e.m. Down arrow (▼) indicates default value. The expected behavior (positive within-object correlation, negative between-objects correlation) emerges over a wide range of parameters. Significant differences between within-object and between-objects correlations occur for an even wider range of parameters. However the behavior breaks down in the absence of lateral connections (top left) or feedback inhibition (top right), confirming that these are the crucial requirements for the emergence of perceptual cycles.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Robustness of system behavior to parameter variation. Correlation between the time-binned PSTH of equally-spaced groups of neurons responding to the same object (blue crosses) and to different objects (red circles), when certain simulation parameters vary while all others remain fixed at their default values. The varying parameters are: (A) strength of EPSP carried by lateral connections; (B) peak amplitude of feedback inhibition; (C) delay between the first spike and the arrival of feedback inhibition; (D) peak amplitude of the after-hyperpolarization potential. All values are averages of 50 runs; error bars indicate s.e.m. Down arrow (▼) indicates default value. The expected behavior (positive within-object correlation, negative between-objects correlation) emerges over a wide range of parameters. Significant differences between within-object and between-objects correlations occur for an even wider range of parameters. However the behavior breaks down in the absence of lateral connections (top left) or feedback inhibition (top right), confirming that these are the crucial requirements for the emergence of perceptual cycles.
Mentions: How robust are these results with regard to the choice of parameters? Figure 4 shows the results of the system when letting certain parameters vary around the default values (keeping all other parameters equal). These figures indicate that cycling behavior occurs over a sizeable range of parameter values. This suggests that the emergence of perceptual cycles is a robust phenomenon, as opposed to a fragile result highly dependent on favorable parameters. In addition, we note that perceptual cycles are eliminated when either lateral connections or feedback inhibition tend toward zero: in both cases, correlations in firing become positive for all neurons, regardless of which object they represent. This confirms that these two mechanisms (feedback inhibition enforcing competitive firing and lateral connections implementing object integration) are the two crucial requirements for the emergence of perceptual cycles in our system.

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.


Related in: MedlinePlus