Neural representation of the bottom-up saliency map of natural scenes in human primary visual cortex

2013 ◽  
Vol 13 (9) ◽  
pp. 233-233
Author(s):  
C. Chen ◽  
X. Zhang ◽  
T. Zhou ◽  
Y. Wang ◽  
F. Fang
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Saliency Map ◽  
Neural Representation ◽  
Natural Scenes ◽  
Bottom Up

A Selective Sparse Coding Model with Embedded Attention Mechanism

2011 ◽  
pp. 78-90
Author(s):  
Qingyong Li ◽  
Zhiping Shi ◽  
Zhongzhi Shi
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Sparse Coding ◽  
Coding Theory ◽  
Neural Representation ◽  
Natural Scenes ◽  
Processing Capacity ◽  
Uniform Sampling ◽  
Coding Efficiency ◽  
Non Uniform Sampling

Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) competing for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding model. This model includes two modules: the non-uniform sampling module simulating the process of retina and a data-driven attention module based on the response saliency. Our experiment results show that the model notably decreases the number of coefficients which may be activated, and retains the main vision information at the same time. It provides a way to improve the coding efficiency for sparse coding model and to achieve good performance in both population sparseness and lifetime sparseness.

scholarly journals A Bioinspired Visual Saliency Model

2019 ◽  
Vol 37 (3) ◽  
pp. 503-508
Author(s):  
Ke Zhang ◽  
Xinbo Zhao ◽  
Rong Mo
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Saliency ◽  
Saliency Map ◽  
Input Image ◽  
Visual Signals ◽  
Bottom Up ◽  
Saliency Model ◽  
Visual Saliency Model ◽  
Public Datasets

This paper presents a bioinspired visual saliency model. The end-stopping mechanism in the primary visual cortex is introduced in to extract features that represent contour information of latent salient objects such as corners, line intersections and line endpoints, which are combined together with brightness, color and orientation features to form the final saliency map. This model is an analog for the processing mechanism of visual signals along from retina, lateral geniculate nucleus(LGN)to primary visual cortex V1:Firstly, according to the characteristics of the retina and LGN, an input image is decomposed into brightness and opposite color channels; Then, the simple cell is simulated with 2D Gabor filters, and the amplitude of Gabor response is utilized to represent the response of complex cell; Finally, the response of an end-stopped cell is obtained by multiplying the response of two complex cells with different orientation, and outputs of V1 and LGN constitute a bottom-up saliency map. Experimental results on public datasets show that our model can accurately predict human fixations, and the performance achieves the state of the art of bottom-up saliency model.

scholarly journals Bottom-up saliency and top-down learning in the primary visual cortex of monkeys

2018 ◽  
Vol 115 (41) ◽  
pp. 10499-10504 ◽  
Author(s):  
Yin Yan ◽  
Li Zhaoping ◽  
Wu Li
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Detection Task ◽  
Late Response ◽  
Late Component ◽  
Neuronal Responses ◽  
Top Down ◽  
Bottom Up ◽  
Response Component ◽  
Orientation Contrast

Early sensory cortex is better known for representing sensory inputs but less for the effect of its responses on behavior. Here we explore the behavioral correlates of neuronal responses in primary visual cortex (V1) in a task to detect a uniquely oriented bar—the orientation singleton—in a background of uniformly oriented bars. This singleton is salient or inconspicuous when the orientation contrast between the singleton and background bars is sufficiently large or small, respectively. Using implanted microelectrodes, we measured V1 activities while monkeys were trained to quickly saccade to the singleton. A neuron’s responses to the singleton within its receptive field had an early and a late component, both increased with the orientation contrast. The early component started from the outset of neuronal responses; it remained unchanged before and after training on the singleton detection. The late component started ∼40 ms after the early one; it emerged and evolved with practicing the detection task. Training increased the behavioral accuracy and speed of singleton detection and increased the amount of information in the late response component about a singleton’s presence or absence. Furthermore, for a given singleton, faster detection performance was associated with higher V1 responses; training increased this behavioral–neural correlate in the early V1 responses but decreased it in the late V1 responses. Therefore, V1’s early responses are directly linked with behavior and represent the bottom-up saliency signals. Learning strengthens this link, likely serving as the basis for making the detection task more reflexive and less top-down driven.

Parallel Advantage: Further Evidence for Bottom-up Saliency Computation by Human Primary Visual Cortex

Perception ◽  
2022 ◽  
Vol 51 (1) ◽  
pp. 60-69
Author(s):  
Li Zhaoping
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Surround Suppression ◽  
Bottom Up ◽  
Black Disk ◽  
V1 Neurons ◽  
Behavioral Observations ◽  
White Disk ◽  
Special Case

Finding a target among uniformly oriented non-targets is typically faster when this target is perpendicular, rather than parallel, to the non-targets. The V1 Saliency Hypothesis (V1SH), that neurons in the primary visual cortex (V1) signal saliency for exogenous attentional attraction, predicts exactly the opposite in a special case: each target or non-target comprises two equally sized disks displaced from each other by 1.2 disk diameters center-to-center along a line defining its orientation. A target has two white or two black disks. Each non-target has one white disk and one black disk, and thus, unlike the target, activates V1 neurons less when its orientation is parallel rather than perpendicular to the neurons’ preferred orientations. When the target is parallel, rather than perpendicular, to the uniformly oriented non-targets, the target’s evoked V1 response escapes V1’s iso-orientation surround suppression, making the target more salient. I present behavioral observations confirming this prediction.

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