Spatial integration in the crustacean visual system: Peripheral and central sources of non-linear summation

1973 ◽  
Vol 13 (10) ◽  
pp. 1801-1814 ◽  
Author(s):  
Raymon Glantz
Keyword(s):  
Visual System ◽  
Spatial Integration ◽  
Linear Summation ◽  
Non Linear

Illusory Reversal of Brightness Contrast

1968 ◽  
Vol 27 (3_suppl) ◽  
pp. 1169-1170 ◽  
Author(s):  
Whitman Richards
Keyword(s):  
Visual System ◽  
Visual Angle ◽  
Low Frequency ◽  
Brightness Contrast ◽  
Linear Behavior ◽  
Non Linear

An illusion analogous to Cornsweet's is used to demonstrate how the non-linear behavior of the visual system can be used to obscure low-frequency gradients. The result is a reversal of brightness—from light to dark—as the visual angle of the display is changed.

Perceived contrast of gratings and plaids: Non-linear summation across oriented filters

1994 ◽  
Vol 34 (8) ◽  
pp. 1061-1075 ◽  
Author(s):  
Mark A. Georgeson ◽  
Trevor M. Shackleton
Keyword(s):  
Linear Summation ◽  
Non Linear

scholarly journals Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

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.

Non-linear summation of responses in averages of rectified EMG

1995 ◽  
Vol 59 (2) ◽  
pp. 175-181 ◽  
Author(s):  
S.N. Baker ◽  
R.N. Lemon
Keyword(s):  
Linear Summation ◽  
Non Linear
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