Representation of the spatial-frequency analysis performed by the visual system*

1975 ◽  
Vol 65 (1) ◽  
pp. 99 ◽  
Author(s):  
G. Gambardella
2018 ◽  
Vol 115 (44) ◽  
pp. 11304-11309 ◽  
Author(s):  
Luciano Dyballa ◽  
Mahmood S. Hoseini ◽  
Maria C. Dadarlat ◽  
Steven W. Zucker ◽  
Michael P. Stryker

Assessments of the mouse visual system based on spatial-frequency analysis imply that its visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 cycles per degree. However, visually mediated behaviors, such as prey capture, suggest that the mouse visual system is more precise. We introduce a stimulus class—visual flow patterns—that is more like what the mouse would encounter in the natural world than are sine-wave gratings but is more tractable for analysis than are natural images. We used 128-site silicon microelectrodes to measure the simultaneous responses of single neurons in the primary visual cortex (V1) of alert mice. While holding temporal-frequency content fixed, we explored a class of drifting patterns of black or white dots that have energy only at higher spatial frequencies. These flow stimuli evoke strong visually mediated responses well beyond those predicted by spatial-frequency analysis. Flow responses predominate in higher spatial-frequency ranges (0.15–1.6 cycles per degree), many are orientation or direction selective, and flow responses of many neurons depend strongly on sign of contrast. Many cells exhibit distributed responses across our stimulus ensemble. Together, these results challenge conventional linear approaches to visual processing and expand our understanding of the mouse’s visual capacity to behaviorally relevant ranges.


2018 ◽  
Author(s):  
Luciano Dyballa ◽  
Mahmood S. Hoseini ◽  
Maria C. Dadarlat ◽  
Steven W. Zucker ◽  
Michael P. Stryker

AbstractAssessments of the mouse visual system based on spatial frequency analysis imply that its visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 cycles/degree. However, visually-mediated behaviors, such as prey capture, suggest that the mouse visual system is more precise. We introduce a new stimulus class—visual flow patterns—that is more like what the mouse would encounter in the natural world than are sine-wave gratings but is more tractable for analysis than are natural images. We used 128-site silicon microelectrodes to measure the simultaneous responses of single neurons in the primary visual cortex (V1) of alert mice. While holding temporal-frequency content fixed, we explored a class of drifting patterns of black or white dots that have energy only at higher spatial frequencies. These flow stimuli evoke strong visually-mediated responses well beyond those predicted by spatial frequency analysis. Flow responses predominate in higher spatial-frequency ranges (0.15–1.6 cycles/degree); many are orientation- or direction-selective; and flow responses of many neurons depend strongly on sign of contrast. Many cells exhibit distributed responses across our stimulus ensemble. Together, these results challenge conventional linear approaches to visual processing and expand our understanding of the mouse’s visual capacity to behaviorally-relevant ranges.Significance StatementThe visual system of the mouse is now widely studied as a model for development and disease in humans. Studies of its primary visual cortex (V1) using conventional grating stimuli to construct linear-nonlinear receptive fields suggest that the mouse must have very poor vision. Using novel stimuli resembling the flow of images across the retina as the mouse moves through the grass, we find that most V1 neurons respond reliably to very much finer details of the visual scene than previously believed. Our findings suggest that the conventional notion of a unique receptive field does not capture the operation of the neural network in mouse V1.


PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235924
Author(s):  
Scott K. Crawford ◽  
Kenneth S. Lee ◽  
Greg R. Bashford ◽  
Bryan C. Heiderscheit

2011 ◽  
Vol 110 (1-2) ◽  
pp. 85-99 ◽  
Author(s):  
Petr Pišoft ◽  
Eva Holtanová ◽  
Peter Huszár ◽  
Jiří Mikšovský ◽  
Michal Žák

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