Evidence map›Paper›PMID 25157228›Full record

ArticleFrontiers in computational neuroscience2014

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

Stephan Tschechne, Heiko Neumann

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. 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 · 2026
    Article
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  7. The QBIT Theory of Consciousness.Integrative psychological & behavioral science · 2020
    Article
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  11. Diversity priors for learning early visual features.Frontiers in computational neuroscience · 2015
    Article
  12. A conceptual framework of computations in mid-level vision.Frontiers in computational neuroscience · 2014
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Stephan TschechneFaculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.
Heiko NeumannFaculty of Engineering and Computer Science (with Psychology and Education), Institute of Neural Information Processing, Ulm University Ulm, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

computational modeldistributed representationfigure-ground segregationmodulatory feedbackventral pathway

Identifiers

PMID25157228
PMCPMC4127482

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.