Visual Spatial Characterization of Macaque V1 Neurons

Sceniak, Michael P.; Hawken, Michael J.; Shapley, Robert

doi:10.1152/jn.2001.85.5.1873

Cited by 271 publications

(354 citation statements)

References 84 publications

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“…Note, each pixel corresponds to a width of 0.8 λ with the 64×64 scale kernel Thus it can be seen that CINNIC has sensitivity for some types of end-stops. This agrees well with research on V1 neurons which shows that most neurons there have some sensitivity to end-stops (Jones et al 2001;Sceniak et al 2001;Pack et al 2003). Additionally it follows a very similar pattern of behavior seen in end-stop neurons in the cat visual cortex.…”

Section: Fig 17supporting

confidence: 91%

Computational modeling and exploration of contour integration for visual saliency

Mundhenk

Itti

2005

Biol Cybern

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We propose a computational model of contour integration for visual saliency. The model uses biologically plausible devices to simulate how the representations of elements aligned collinearly along a contour in an image are enhanced. Our model adds such devices as a dopamine-like fast plasticity, local GABAergic inhibition and multi-scale processing of images. The fast plasticity addresses the problem of how neurons in visual cortex seem to be able to influence neurons they are not directly connected to, for instance, as observed in contour closure effect. Local GABAergic inhibition is used to control gain in the system without using global mechanisms which may be non-plausible given the limited reach of axonal arbors in visual cortex. The model is then used to explore not only its validity in real and artificial images, but to discover some of the mechanisms involved in processing of complex visual features such as junctions and end-stops as well as contours. We present evidence for the validity of our model in several phases, starting with local enhancement of only a few collinear elements. We then test our model on more complex contour integration images with a large number of Gabor elements. Sections of the model are also extracted and used to discover how the model might relate contour integration neurons to neurons that process end-stops and junctions. Finally, we present results from real world images. Results from the model suggest that it is a good current approximation of contour integration in human vision. As well, it suggests that contour integration mechanisms may be strongly related to mechanisms for detecting end-stops and junction points. Additionally, a contour integration mechanism may be involved in finding features for objects such as faces. This suggests that visual cortex may be more information efficient and that neural regions may have multiple roles.

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Section: Fig 17supporting

confidence: 91%

Computational modeling and exploration of contour integration for visual saliency

Mundhenk

Itti

2005

Biol Cybern

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“…S3 and S4). These results are consistent with the observation that the subtractive and divisive models yield similar estimates of the extent of the center and surround (Sceniak et al 2001). We therefore use the subtractive DOG models throughout the present study.…”

Section: Reconstruction Of the Center-surround Structuresupporting

confidence: 88%

Surround Suppression of V1 Neurons Mediates Orientation-Based Representation of High-Order Visual Features

Tanaka

Ohzawa²

2009

Journal of Neurophysiology

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Tanaka H, Ohzawa I. Surround suppression of V1 neurons mediates orientation-based representation of high-order visual features. J Neurophysiol 101: 1444 -1462, 2009. First published December 31, 2008 doi:10.1152/jn.90749.2008. Neurons with surround suppression have been implicated in processing high-order visual features such as contrast-or texture-defined boundaries and subjective contours. However, little is known regarding how these neurons encode high-order visual information in a systematic manner as a population. To address this issue, we have measured detailed spatial structures of classical center and suppressive surround regions of receptive fields of primary visual cortex (V1) neurons and examined how a population of such neurons allow encoding of various high-order features and shapes in visual scenes. Using a novel method to reconstruct structures, we found that the center and surround regions are often both elongated parallel to each other, reminiscent of ON and OFF subregions of simple cells without surround suppression. These structures allow V1 neurons to extract high-order contours of various orientations and spatial frequencies, with a variety of optimal values across neurons. The results show that a wide range of orientations and widths of the high-order features are systematically represented by the population of V1 neurons with surround suppression.

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“…6). The assignment of each unit to a specific cortical layer was made on the basis of the normalized depths of recording sites obtained from Nissl-stained sections and the histological reconstruction of electrode tracks that contained three electrolytic lesions along the track (12,21). Because the sample size was not large enough to examine each cortical layer, we assigned units to three categories, i.e., the upper (I-IVab), input (IVc), and lower layers (V-VI).…”

Section: Resultsmentioning

confidence: 99%

“…In normal adult monkeys, the long-range connections provide substrates for the subthreshold modulatory influences on V1 neuron's responses when relatively large areas outside the classic RF are stimulated (21,32,33,(36)(37)(38)(39)(40). The size of the gratings used in this study was 4.5°in diameter, which was sufficiently large to stimulate the RF surrounds of the majority of V1 neurons with RFs within the central 5-6° (38)(39)(40).…”

Section: Discussionmentioning

confidence: 98%

Rapid plasticity of binocular connections in developing monkey visual cortex (V1)

Zhang

Bai

Sakai

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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The basic sets of cortical connections are present at birth in the primate visual system. The maintenance and refinement of these innate connections are highly dependent on normal visual experience, and prolonged exposure to binocularly uncorrelated signals early in life severely disrupts the normal development of binocular functions. However, very little is known about how rapidly these changes in the functional organization of primate visual cortex emerge or what are the sequence and the nature of the abnormal neural events that occur immediately after experiencing binocular decorrelation. In this study, we investigated how brief periods of ocular misalignment (strabismus) at the height of the critical period alter the cortical circuits that support binocular vision. After only 3 days of optically imposed strabismus, there was a striking increase in the prevalence of V1 neurons that exhibited binocular suppression, i.e., binocular responses were weaker than monocular responses. However, the sensitivity of these neurons to interocular spatial phase disparity was not significantly altered. These contrasting results suggest that the first significant change in V1 caused by early binocular decorrelation is binocular suppression, and that this suppression originates at a site(s) beyond where binocular signals are initially combined. interocular suppression ͉ long-range interactions ͉ macaque monkeys ͉ strabismus

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Visual Spatial Characterization of Macaque V1 Neurons

Cited by 271 publications

References 84 publications

Computational modeling and exploration of contour integration for visual saliency

Computational modeling and exploration of contour integration for visual saliency

Surround Suppression of V1 Neurons Mediates Orientation-Based Representation of High-Order Visual Features

Rapid plasticity of binocular connections in developing monkey visual cortex (V1)

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