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Cited by 426Visual perception and processing mechanismsNeural dynamics and brain functionNeural Networks and Applications

A Neural Model of Contour Integration in the Primary Visual Cortex

Open access
Li, Zhaoping·1998·Neural Computation
DOI 10.1162/089976698300017557
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Abstract

Experimental observations suggest that contour integration may take place in V1. However, there has yet to be a model of contour integration that uses only known V1 elements, operations, and connection patterns.This article introduces such a model, using orient ation selective cells, local cortical circuits, and horizontal intracortical connections. The model is composed of recurrently connected excitatory neurons and inhibitory interneurons, receiving visual input via oriented receptive fields resembling those found in primary visual cortex. Intracortical interactions modify initial activity patterns from input, selectively amplifying the activities of edges that form smooth contours in the image. The neural activities produced by such interactions are oscillatory and edge segments within a contour oscillate in synchrony. It is shown analytically and empirically that the extent of contour enhancement and neural synchrony increases with the smoothness, length, and closure of contours, as observed in experiments on some of these phenomena. In addition, the model incorporates a feedback mechanism that allows higher visual centers selectively to enhance or suppress sensitivities to given contours, effectively segmenting one from another. The model makes the testable prediction that the horizontal cortical connections are more likely to target excitatory (or inhibitory) cells when the two linked cells have their preferred orientation aligned with (or orthogonal to) their relative receptive field center displacements.

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  • Instead of being the first stage in a feedforward pipeline, V1 is better described as the unique high-resolution buffer in the visual system for geometric calculations.

    “This led us to propose that, instead of being the first stage in a feedforward pipeline, 13 V1 is better described as the unique high-resolution buffer in the visual system for geometric calculations. 40,41”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [41]

    The Introduction and §4.1 describe V1 as performing local contour enhancement through recurrent interactions, rather than merely passing visual input forward. Section 3.7 also models feedback from higher visual areas. The paper does not describe V1 as the visual system’s unique high-resolution buffer or extend its contour-enhancement model to geometric calculations generally. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 34

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Authors
Li, Z.
Affiliations
Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Venue
Neural Computation
Publisher
MIT Press - Journals
Published
1998-05-01
Volume
10
Issue
4
Pages
903-940
ISSN
  • 0899-7667 (ISSN-L)
  • 1530-888X
DOI
10.1162/089976698300017557
OpenAlex
W2134394617
Cited by
426
References
2
Citations by year
2026: 3 · 2025: 9 · 2024: 12 · 2023: 7 · 2022: 5 · 2021: 6 · 2020: 14 · 2019: 7 · 2018: 10 · 2017: 18 · 2016: 12 · 2015: 13 · 2014: 31 · 2013: 15 · 2012: 10
FWCI
11.68
Type
journal article
Language
en
Open access
Locations
  • Neural Computation (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
contour integration, primary visual cortex, recurrent neural networks, inhibitory interneurons, neuronal synchronization, contour closure, feedback mechanisms, contour segmentation, receptive fields
MeSH terms
Feedback, Form Perception, Mental Processes, Models, Neurological, Oscillometry, Time Factors, Visual Cortex, Linear Models

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