scholarly journals Surround Suppression in Attention to Spatial Frequency

2019 ◽  
Vol 19 (10) ◽  
pp. 270c
Author(s):  
Ming W.H. Fang ◽  
Taosheng Liu
Keyword(s):  
Spatial Frequency ◽  
Surround Suppression

scholarly journals Surround suppression in visual cortex: Effects of spatial frequency

2010 ◽  
Vol 9 (8) ◽  
pp. 977-977
Author(s):  
A. B. Sekuler ◽  
L. R. Betts ◽  
E. Roudaia ◽  
Y. Konar ◽  
P. J. Bennett
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Surround Suppression

scholarly journals Spatial frequency bandwidth of surround suppression tuning curves

2012 ◽  
Vol 12 (6) ◽  
pp. 24-24 ◽  
Author(s):  
I. Serrano-Pedraza ◽  
J. P. Grady ◽  
J. C. A. Read
Keyword(s):  
Spatial Frequency ◽  
Frequency Bandwidth ◽  
Surround Suppression ◽  
Tuning Curves

Surround suppression by high spatial frequency stimuli in the cat primary visual cortex

2011 ◽  
Vol 33 (5) ◽  
pp. 923-932 ◽  
Author(s):  
Hironobu Osaki ◽  
Tomoyuki Naito ◽  
Osamu Sadakane ◽  
Masahiro Okamoto ◽  
Hiromichi Sato
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Primary Visual Cortex ◽  
High Spatial Frequency ◽  
Surround Suppression

scholarly journals Spatial frequency bandwidth of surround suppression

2011 ◽  
Vol 11 (11) ◽  
pp. 1177-1177
Author(s):  
I. Serrano-Pedraza ◽  
J. P. Grady ◽  
J. C. A. Read
Keyword(s):  
Spatial Frequency ◽  
Frequency Bandwidth ◽  
Surround Suppression

scholarly journals Spatiotemporal dynamics of surround suppression in cat V1: spatial-frequency dependency

2010 ◽  
Vol 6 (6) ◽  
pp. 892-892
Author(s):  
A. Ishikawa ◽  
S. Shimegi ◽  
H. Kida ◽  
H. Sato
Keyword(s):  
Spatial Frequency ◽  
Spatiotemporal Dynamics ◽  
Surround Suppression ◽  
Frequency Dependency

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

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