scholarly journals Timing Precision in Population Coding of Natural Scenes in the Early Visual System

PLoS Biology ◽  
2008 ◽  
Vol 6 (12) ◽  
pp. e324 ◽  
Author(s):  
Gaëlle Desbordes ◽  
Jianzhong Jin ◽  
Chong Weng ◽  
Nicholas A Lesica ◽  
Garrett B Stanley ◽  
...  
Keyword(s):  
Visual System ◽  
Population Coding ◽  
Natural Scenes ◽  
Timing Precision ◽  
Early Visual System

Population coding of motion patterns in the early visual system

2001 ◽  
Vol 187 (7) ◽  
pp. 549-558 ◽  
Author(s):  
Stefan Wilke ◽  
Andreas Thiel ◽  
Christian Eurich ◽  
Martin Greschner ◽  
Markus Bongard ◽  
...  
Keyword(s):  
Visual System ◽  
Population Coding ◽  
Motion Patterns ◽  
Early Visual System

Independence of luminance and contrast in natural scenes and in the early visual system

2005 ◽  
Vol 8 (12) ◽  
pp. 1690-1697 ◽  
Author(s):  
Valerio Mante ◽  
Robert A Frazor ◽  
Vincent Bonin ◽  
Wilson S Geisler ◽  
Matteo Carandini
Keyword(s):  
Visual System ◽  
Natural Scenes ◽  
Early Visual System

scholarly journals A silicon early visual system as a model animal

2004 ◽  
Vol 44 (17) ◽  
pp. 2083-2089 ◽  
Author(s):  
Tobi Delbrück ◽  
Shih-Chii Liu
Keyword(s):  
Visual System ◽  
Model Animal ◽  
Early Visual System

scholarly journals Transformation from independent to integrative coding of multi-object arrangements in human visual cortex

2017 ◽  
Author(s):  
Daniel Kaiser ◽  
Marius V. Peelen
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Activity Patterns ◽  
Spatial Arrangement ◽  
Living Room ◽  
Natural Scenes ◽  
Response Patterns ◽  
Object Representations ◽  
Individual Object ◽  
Object Coding

AbstractTo optimize processing, the human visual system utilizes regularities present in naturalistic visual input. One of these regularities is the relative position of objects in a scene (e.g., a sofa in front of a television), with behavioral research showing that regularly positioned objects are easier to perceive and to remember. Here we use fMRI to test how positional regularities are encoded in the visual system. Participants viewed pairs of objects that formed minimalistic two-object scenes (e.g., a “living room” consisting of a sofa and television) presented in their regularly experienced spatial arrangement or in an irregular arrangement (with interchanged positions). Additionally, single objects were presented centrally and in isolation. Multi-voxel activity patterns evoked by the object pairs were modeled as the average of the response patterns evoked by the two single objects forming the pair. In two experiments, this approximation in object-selective cortex was significantly less accurate for the regularly than the irregularly positioned pairs, indicating integration of individual object representations. More detailed analysis revealed a transition from independent to integrative coding along the posterior-anterior axis of the visual cortex, with the independent component (but not the integrative component) being almost perfectly predicted by object selectivity across the visual hierarchy. These results reveal a transitional stage between individual object and multi-object coding in visual cortex, providing a possible neural correlate of efficient processing of regularly positioned objects in natural scenes.

scholarly journals Computing Optical Flow in the Primate Visual System

1989 ◽  
Vol 1 (1) ◽  
pp. 92-103 ◽  
Author(s):  
H. Taichi Wang ◽  
Bimal Mathur ◽  
Christof Koch
Keyword(s):  
Visual System ◽  
Optical Flow ◽  
Difficult Problem ◽  
Population Coding ◽  
Spatial Integration ◽  
Local Motion ◽  
Temporal Area ◽  
Second Stage ◽  
Vector Sum ◽  
Cooperative Algorithm

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We show how gradient models, a well-known class of motion algorithms, can be implemented within the magnocellular pathway of the primate's visual system. Our cooperative algorithm computes optical flow in two steps. In the first stage, assumed to be located in primary visual cortex, local motion is measured while spatial integration occurs in the second stage, assumed to be located in the middle temporal area (MT). The final optical flow is extracted in this second stage using population coding, such that the velocity is represented by the vector sum of neurons coding for motion in different directions. Our theory, relating the single-cell to the perceptual level, accounts for a number of psychophysical and electrophysiological observations and illusions.

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