ArticleFrontiers in computational neuroscience2014
Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Synergistic Geniculate and Cortical Dynamics Facilitate a Decorrelated Spatial Frequency Code in the Early Visual System.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- A model of thalamo-cortical interaction for incremental binding in mental contour-tracing.PLoS computational biology · 2025Article
- Deep multimodal representations and classification of first-episode psychosis via live face processing.Frontiers in psychiatry · 2025Article
- Teaching deep networks to see shape: Lessons from a simplified visual world.PLoS computational biology · 2024Article
- Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.bioRxiv : the preprint server for biology · 2024Article
- Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.PLoS computational biology · 2024Article
- The QBIT Theory of Consciousness.Integrative psychological & behavioral science · 2020Article
- A recurrent neural model for proto-object based contour integration and figure-ground segregation.Journal of computational neuroscience · 2017Article
- Article
- Editorial: Hierarchical Object Representations in the Visual Cortex and Computer Vision.Frontiers in computational neuroscience · 2015Article
- Diversity priors for learning early visual features.Frontiers in computational neuroscience · 2015Article
- A conceptual framework of computations in mid-level vision.Frontiers in computational neuroscience · 2014Article
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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.
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