Beáta Reiz

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Róbert Busa-Fekete

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Sándor Pongor

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Ilona Kovács

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The primary visual cortex (V1) of the mammalian brain is equipped with a specifically connected network of neurons that can potentially solve difficult image processing tasks. These neurons are selectively tuned for locations in visual space and also for line orientation. The coupling of location and orientation tuning results in the neural representation of the visual world in terms of local features. These local features, e.g., oriented line segments, will have to be linked together in order to parse the visual world into regions corresponding to object and ground. Although standard models of V1 do not address the issue of interacting neuronal populations, we suggest that the long-range connectivity pattern of V1 provides an architecture where spreading neural activity may lead to pertinent figure-ground segmentation. The model relies on the fact that in addition to the processing units, their connections are also selectively tuned for space and orientation. From the computational point of view, the model uses a minimalist approach that applies the fundamental concepts of Gestalt psychology – proximity, similarity and continuity – to the spreading of neuronal activation signals. This model is successful in predicting psychophysical performance of human observers, and provides an account of the computational power of V1.

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Learning & Perception
Language English
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Publisher Akadémiai Kiadó
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
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ISSN 1789-3186 (Print)
ISSN 2060-9175 (Online)

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