Temporal dynamics of 2D and 3D shape representation in macaque visual area V4

2006 ◽  
Vol 23 (5) ◽  
pp. 749-763 ◽  
Author(s):  
JAY HEGDÉ ◽  
DAVID C. VAN ESSEN
Keyword(s):  
Temporal Dynamics ◽  
Shape Representation ◽  
Macaque Monkey ◽  
Large Stimulus ◽  
3D Shape ◽  
Area V4 ◽  
Single Animal ◽  
Distributed Process ◽  
V4 Neurons ◽  
2D And 3D

We studied the temporal dynamics of shape representation in area V4 of the alert macaque monkey. Analyses were based on two large stimulus sets, one equivalent to the 2D shape stimuli used in a previous study of V2, and the other a set of stereoscopic 3D shape stimuli. As in V2, we found that information conveyed by individual V4 neurons about the stimuli tended to be maximal during the initial transient response and generally lower, albeit statistically significant, afterwards. The population response was substantially correlated from one stimulus to the next during the transients, and decorrelated as responses decayed. V4 responses showed significantly longer latencies than in V2, especially for the 3D stimulus set. Recordings from area V1 in a single animal revealed temporal dynamic patterns in response to the 2D shape stimuli that were largely similar to those in V2 and V4. Together with earlier results, these findings provide evidence for a distributed process of coarse-to-fine representation of shape stimuli in the visual cortex.

scholarly journals Spectral receptive fields do not explain tuning for boundary curvature in V4

2014 ◽  
Vol 112 (9) ◽  
pp. 2114-2122 ◽  
Author(s):  
Timothy D. Oleskiw ◽  
Anitha Pasupathy ◽  
Wyeth Bair
Keyword(s):  
Shape Representation ◽  
Angular Position ◽  
Receptive Fields ◽  
Shape Selectivity ◽  
Neural Representation ◽  
Phase Information ◽  
Area V4 ◽  
Visual Cortical Area ◽  
V4 Neurons ◽  
Contour Curvature

The midlevel visual cortical area V4 in the primate is thought to be critical for the neural representation of visual shape. Several studies agree that V4 neurons respond to contour features, e.g., convexities and concavities along a shape boundary, that are more complex than the oriented segments encoded by neurons in the primary visual cortex. Here we compare two distinct approaches to modeling V4 shape selectivity: one based on a spectral receptive field (SRF) map in the orientation and spatial frequency domain and the other based on a map in an object-centered angular position and contour curvature space. We test the ability of these two characterizations to account for the responses of V4 neurons to a set of parametrically designed two-dimensional shapes recorded previously in the awake macaque. We report two lines of evidence suggesting that the SRF model does not capture the contour sensitivity of V4 neurons. First, the SRF model discards spatial phase information, which is inconsistent with the neuronal data. Second, the amount of variance explained by the SRF model was significantly less than that explained by the contour curvature model. Notably, cells best fit by the curvature model were poorly fit by the SRF model, the latter being appropriate for a subset of V4 neurons that appear to be orientation tuned. These limitations of the SRF model suggest that a full understanding of midlevel shape representation requires more complicated models that preserve phase information and perhaps deal with object segmentation.

scholarly journals Temporally evolving gain mechanisms of attention in macaque area V4

2017 ◽  
Vol 118 (2) ◽  
pp. 964-985 ◽  
Author(s):  
Ilaria Sani ◽  
Elisa Santandrea ◽  
Maria Concetta Morrone ◽  
Leonardo Chelazzi
Keyword(s):  
Visual Processing ◽  
Temporal Dynamics ◽  
Luminance Contrast ◽  
Response Functions ◽  
Fundamental Factor ◽  
Area V4 ◽  
V4 Neurons ◽  
Contrast Response

We offer an innovative perspective on the interplay between attention and luminance contrast in macaque area V4, one in which time becomes a fundamental factor. We place emphasis on the temporal dynamics of attentional effects, pioneering the notion that attention modulates contrast response functions of V4 neurons via the sequential engagement of distinct gain mechanisms. These findings advance understanding of attentional influences on visual processing and help reconcile divergent results in the literature.

scholarly journals Effects of stereoscopic disparity on early ERP components during classification of three-dimensional objects

2018 ◽  
Vol 71 (6) ◽  
pp. 1419-1430 ◽  
Author(s):  
Alan J Pegna ◽  
Alexandra Darque ◽  
Mark V Roberts ◽  
E Charles Leek
Keyword(s):  
Image Classification ◽  
Temporal Dynamics ◽  
Time Window ◽  
Three Dimensional ◽  
Event Related Potential ◽  
Differential Sensitivity ◽  
Object Shape ◽  
3D Object ◽  
3D Shape ◽  
2D And 3D

This study investigates the effects of stereo disparity on the perception of three-dimensional (3D) object shape. We tested the hypothesis that stereo input modulates the brain activity related to perceptual analyses of 3D shape configuration during image classification. High-density (256-channel) electroencephalogram (EEG) was used to record the temporal dynamics of visual shape processing under conditions of two-dimensional (2D) and 3D visual presentation. On each trial, observers made image classification judgements (‘Same’/’Different’) to two briefly presented, multi-part, novel objects. On different-object trials, stimuli could either share volumetric parts but not the global 3D shape configuration and have different parts but the same global 3D shape configuration or differ on both aspects. Analyses using mass univariate contrasts showed that the earliest sensitivity to 2D versus 3D viewing appeared as a negative deflection over posterior locations on the N1 component between 160 and 220 ms post-stimulus onset. Subsequently, event-related potential (ERP) modulations during the N2 time window between 240 and 370 ms were linked to image classification. N2 activity reflected two distinct components – an early N2 (240-290 ms) and a late N2 (290-370 ms) – that showed different patterns of responses to 2D and 3D input and differential sensitivity to 3D object structure. The results revealed that stereo input modulates the neural correlates of 3D object shape. We suggest that this reflects differential perceptual processing of object shape under conditions of stereo or mono input. These findings challenge current theories that attribute no functional role for stereo input during 3D shape perception.

scholarly journals Curvature domains in V4 of macaque monkey

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jia Ming Hu ◽  
Xue Mei Song ◽  
Qiannan Wang ◽  
Anna Wang Roe
Keyword(s):  
Shape Representation ◽  
Large Body ◽  
Macaque Monkey ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Intrinsic Signal ◽  
Area V4 ◽  
Complex Orientation ◽  
Complex Shapes ◽  
Complex Curvature

An important aspect of visual object recognition is the ability to perceive object shape. Two basic components of complex shapes are straight and curved contours. A large body of evidence suggests a modular hierarchy for shape representation progressing from simple and complex orientation in early areas V1 and V2, to increasingly complex stages of curvature representation in V4, TEO, and TE. Here, we reinforce and extend the concept of modular representation. Using intrinsic signal optical imaging in Macaque area V4, we find sub-millimeter sized modules for curvature representation that are organized from low to high curvatures as well as domains with complex curvature preference. We propose a possible ‘curvature hypercolumn’ within V4. In combination with previous studies, we suggest that the key emergent functions at each stage of cortical processing are represented in systematic, modular maps.

scholarly journals MeshNet: Mesh Neural Network for 3D Shape Representation

2019 ◽  
Vol 33 ◽  
pp. 8279-8286 ◽  
Author(s):  
Yutong Feng ◽  
Yifan Feng ◽  
Haoxuan You ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Point Cloud ◽  
State Of The Art ◽  
Shape Representation ◽  
Shape Classification ◽  
Retrieval Performance ◽  
3D Shape ◽  
General Architecture ◽  
3D Shapes

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

One-Pot and One-Step Fabrication of Salt-Responsive Bilayer Hydrogels with 2D and 3D Shape Transformations

2019 ◽  
Vol 11 (28) ◽  
pp. 25417-25426 ◽  
Author(s):  
Xiaomin He ◽  
Dong Zhang ◽  
Jiahui Wu ◽  
Yang Wang ◽  
Feng Chen ◽  
...  
Keyword(s):  
One Pot ◽  
3D Shape ◽  
Shape Transformations ◽  
One Step ◽  
2D And 3D

Differential Temporal Storage Capacity in the Baseline Activity of Neurons in Macaque Frontal Eye Field and Area V4

2010 ◽  
Vol 103 (5) ◽  
pp. 2433-2445 ◽  
Author(s):  
Tadashi Ogawa ◽  
Hidehiko Komatsu
Keyword(s):  
Neuronal Activity ◽  
Storage Capacity ◽  
Discharge Rate ◽  
Time Lag ◽  
Temporal Correlation ◽  
Frontal Eye Field ◽  
Area V4 ◽  
Baseline Activity ◽  
Eye Field ◽  
V4 Neurons

Previous studies have suggested that spontaneous fluctuations in neuronal activity reflect intrinsic functional brain architecture. Inspired by these findings, we analyzed baseline neuronal activity in the monkey frontal eye field (FEF; a visuomotor area) and area V4 (a visual area) during the fixation period of a cognitive behavioral task in the absence of any task-specific stimuli or behaviors. Specifically, we examined the temporal storage capacity of the instantaneous discharge rate in FEF and V4 neurons by calculating the correlation of the spike count in a bin with that in another bin during the baseline activity of a trial. We found that most FEF neurons fired significantly more (or less) in one bin if they fired more (or less) in another bin within a trial, even when these two time bins were separated by hundreds of milliseconds. By contrast, similar long time-lag correlations were observed in only a small fraction of V4 neurons, indicating that temporal correlations were considerably stronger in FEF compared with those in V4 neurons. Additional analyses revealed that the findings were not attributable to other task-related variables or ongoing behavioral performance, suggesting that the differences in temporal correlation strength reflect differences in intrinsic structural and functional architecture between visual and visuomotor areas. Thus FEF neurons probably play a greater role than V4 neurons in neural circuits responsible for temporal storage in activity.

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