Contour detection model based on neuron behaviour in primary visual cortex

2018 ◽  
Vol 12 (6) ◽  
pp. 863-872 ◽  
Author(s):  
Chuan Lin ◽  
Guili Xu ◽  
Yijun Cao
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Contour Detection ◽  
Detection Model ◽  
Model Based

Analysis of Direction Selectivity Arising from Recurrent Cortical Interactions

1998 ◽  
Vol 10 (2) ◽  
pp. 353-371 ◽  
Author(s):  
Paul Mineiro ◽  
David Zipser
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Detailed Comparison ◽  
Direction Selectivity ◽  
Dynamical Model ◽  
Physiological Data ◽  
Model Based ◽  
The Subject ◽  
Neuroscience Community

The relative contributions of feedforward and recurrent connectivity to the direction-selective responses of cells in layer IVB of primary visual cortex are currently the subject of debate in the neuroscience community. Recently, biophysically detailed simulations have shown that realistic direction-selective responses can be achieved via recurrent cortical interactions between cells with nondirection-selective feedforward input (Suarez et al., 1995; Maex & Orban, 1996). Unfortunately these models, while desirable for detailed comparison with biology, are complex and thus difficult to analyze mathematically. In this article, a relatively simple cortical dynamical model is used to analyze the emergence of direction-selective responses via recurrent interactions. A comparison between a model based on our analysis and physiological data is presented. The approach also allows analysis of the recurrently propagated signal, revealing the predictive nature of the implementation.

scholarly journals CONTOUR DETECTION MODEL BASED ON BIOLOGICAL VISUAL PERCEPTION

2009 ◽  
Vol 28 (5) ◽  
pp. 362-366 ◽  
Author(s):  
Zhao-Yu PIAN ◽  
Xiang-Ping MENG
Keyword(s):  
Visual Perception ◽  
Contour Detection ◽  
Detection Model ◽  
Model Based

Improved Color Opponent Contour Detection Model Based on Dark and Light Adaptation

2019 ◽  
Vol 53 (6) ◽  
pp. 560-571 ◽  
Author(s):  
Chuan Lin ◽  
Hao-Jun Zhao ◽  
Yi-Jun Cao
Keyword(s):  
Light Adaptation ◽  
Contour Detection ◽  
Detection Model ◽  
Model Based

scholarly journals Stereoscopic Depth Perception Using a Model Based on the Primary Visual Cortex

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e80745 ◽  
Author(s):  
Fernanda da C. e C. Faria ◽  
Jorge Batista ◽  
Helder Araújo
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Depth Perception ◽  
Stereoscopic Depth ◽  
Model Based

scholarly journals Bio-inspired contour detection model based on multi-bandwidth fusion and logarithmic texture inhibition

2019 ◽  
Vol 13 (12) ◽  
pp. 2304-2313 ◽  
Author(s):  
Chuan Lin ◽  
Fuzhang Li ◽  
Yijun Cao ◽  
Haojun Zhao
Keyword(s):  
Contour Detection ◽  
Detection Model ◽  
Model Based

scholarly journals Axonal plasticity associated with perceptual learning in adult macaque primary visual cortex

2018 ◽  
Vol 115 (41) ◽  
pp. 10464-10469 ◽  
Author(s):  
Timo van Kerkoerle ◽  
Sally A. Marik ◽  
Stephan Meyer zum Alten Borgloh ◽  
Charles D. Gilbert
Keyword(s):  
Visual Cortex ◽  
Perceptual Learning ◽  
Primary Visual Cortex ◽  
Detection Task ◽  
Contour Detection ◽  
Cortical Lesion ◽  
Axon Collaterals ◽  
Horizontal Connections ◽  
Cortical Regions ◽  
Axonal Arbors

Perceptual learning is associated with changes in the functional properties of neurons even in primary sensory areas. In macaque monkeys trained to perform a contour detection task, we have observed changes in contour-related facilitation of neuronal responses in primary visual cortex that track their improvement in performance on a contour detection task. We have previously explored the anatomical substrate of experience-dependent changes in the visual cortex based on a retinal lesion model, where we find sprouting and pruning of the axon collaterals in the cortical lesion projection zone. Here, we attempted to determine whether similar changes occur under normal visual experience, such as that associated with perceptual learning. We labeled the long-range horizontal connections in visual cortex by virally mediated transfer of genes expressing fluorescent probes, which enabled us to do longitudinal two-photon imaging of axonal arbors over the period during which animals improve in contour detection performance. We found that there are substantial changes in the axonal arbors of neurons in cortical regions representing the trained part of the visual field, with sprouting of new axon collaterals and pruning of preexisting axon collaterals. Our findings indicate that changes in the structure of axonal arbors are part of the circuit-level mechanism of perceptual learning, and further support the idea that the learned information is encoded at least in part in primary visual cortex.

A model of contextual interactions and contour detection in primary visual cortex

2004 ◽  
Vol 17 (5-6) ◽  
pp. 719-735 ◽  
Author(s):  
Mauro Ursino ◽  
Giuseppe Emiliano La Cara
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Contour Detection ◽  
Contextual Interactions

scholarly journals Model-based characterization of the selectivity of neurons in primary visual cortex

2021 ◽  
Author(s):  
Felix Bartsch ◽  
Bruce G Cumming ◽  
Daniel A Butts
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Frequency Tuning ◽  
Nonlinear Models ◽  
Field Size ◽  
V1 Neurons ◽  
Model Based ◽  
Spatial Frequencies

To understand the complexity of stimulus selectivity in primary visual cortex (V1), models constructed to match observed responses to complex time-varying stimuli, instead of to explain responses to simple parametric stimuli, are increasingly used. While such models often can more accurately reflect the computations performed by V1 neurons in more natural visual environments, they do not by themselves provide insight into established measures of V1 neural selectivity such as receptive field size, spatial frequency tuning and phase invariance. Here, we suggest a series of analyses that can be directly applied to encoding models to link complex encoding models to more interpretable aspects of stimulus selectivity, applied to nonlinear models of V1 neurons recorded in awake macaque in response to random bar stimuli. In linking model properties to more classical measurements, we demonstrate several novel aspects of V1 selectivity not available to simpler experimental measurements. For example, we find that individual spatiotemporal elements of the V1 models often have a smaller spatial scale than the overall neuron sensitivity, and that this results in non-trivial tuning to spatial frequencies. Additionally, our proposed measures of nonlinear integration suggest that more classical classifications of V1 neurons into simple versus complex cells are spatial-frequency dependent. In total, rather than obfuscate classical characterizations of V1 neurons, model-based characterizations offer a means to more fully understand their selectivity, and provide a means to link their classical tuning properties to their roles in more complex, natural, visual processing.

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