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Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

Tschechne S, Neumann H - Front Comput Neurosci (2014)

Bottom Line: Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour.Our model is probed with a selection of stimuli to illustrate processing results at different processing stages.We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.

ABSTRACT
Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

No MeSH data available.


Related in: MedlinePlus

Effects of feedback on the signal flow. The four table cells illustrate the effects when a feedback signal and/or an input signal is available. Please note that a feedback signal alone cannot elicit any cell response in the modeled area. It only enhances the response level when the filtering of the input signal generates some output.
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Figure 2: Effects of feedback on the signal flow. The four table cells illustrate the effects when a feedback signal and/or an input signal is available. Please note that a feedback signal alone cannot elicit any cell response in the modeled area. It only enhances the response level when the filtering of the input signal generates some output.

Mentions: At the second stage of the cascade, response levels are modulated by recurring input from higher visual areas. We propose a feedback mechanism that excerpts a purely modulatory gain control on the input. That means that feedback alone cannot generate activities without activation by the initial filtering step (see Figure 2). With R being the unmodulated driving signal and netFB being the strength of the feedback, the modulated response is


Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

Tschechne S, Neumann H - Front Comput Neurosci (2014)

Effects of feedback on the signal flow. The four table cells illustrate the effects when a feedback signal and/or an input signal is available. Please note that a feedback signal alone cannot elicit any cell response in the modeled area. It only enhances the response level when the filtering of the input signal generates some output.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127482&req=5

Figure 2: Effects of feedback on the signal flow. The four table cells illustrate the effects when a feedback signal and/or an input signal is available. Please note that a feedback signal alone cannot elicit any cell response in the modeled area. It only enhances the response level when the filtering of the input signal generates some output.
Mentions: At the second stage of the cascade, response levels are modulated by recurring input from higher visual areas. We propose a feedback mechanism that excerpts a purely modulatory gain control on the input. That means that feedback alone cannot generate activities without activation by the initial filtering step (see Figure 2). With R being the unmodulated driving signal and netFB being the strength of the feedback, the modulated response is

Bottom Line: Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour.Our model is probed with a selection of stimuli to illustrate processing results at different processing stages.We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.

ABSTRACT
Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

No MeSH data available.


Related in: MedlinePlus