Different spatial frequency bands selectively signal for natural image statistics in the early visual system

2012 ◽  
Vol 108 (8) ◽  
pp. 2160-2172 ◽  
Author(s):  
Bruce C. Hansen ◽  
Aaron P. Johnson ◽  
Dave Ellemberg

Early visual evoked potentials (VEPs) measured in humans have recently been observed to be modulated by the image statistics of natural scene imagery. Specifically, the early VEP is dominated by a strong positivity when participants view minimally complex natural scene imagery, with the magnitude of that component being modulated by luminance contrast differences across spatial frequency (i.e., the slope of the amplitude spectrum). For scenes high in structural complexity, the early VEP is dominated by a prominent negativity that exhibits little dependency on luminance contrast. However, since natural scene imagery is broad band in terms of spatial frequency, it is not known whether the above-mentioned modulation results from a complex interaction within or between the early neural processes tuned to different bands of spatial frequency. Here, we sought to address this question by measuring early VEPs (specifically, the C1, P1, and N1 components) while human participants viewed natural scene imagery that was filtered to contain specific bands of spatial frequency information. The results show that the C1 component is largely unmodulated by the luminance statistics of natural scene imagery (being only measurable when such stimuli were made to contain high spatial frequencies). The P1 and N1, on the other hand, were observed to exhibit strong spatial frequency-dependent modulation to the luminance statistics of natural scene imagery. The results therefore suggest that the dependency of early VEPs on natural image statistics results from an interaction between the early neural processes tuned to different bands of spatial frequency.

2017 ◽  
Vol 29 (10) ◽  
pp. 2769-2799 ◽  
Author(s):  
P. N. Loxley

The two-dimensional Gabor function is adapted to natural image statistics, leading to a tractable probabilistic generative model that can be used to model simple cell receptive field profiles, or generate basis functions for sparse coding applications. Learning is found to be most pronounced in three Gabor function parameters representing the size and spatial frequency of the two-dimensional Gabor function and characterized by a nonuniform probability distribution with heavy tails. All three parameters are found to be strongly correlated, resulting in a basis of multiscale Gabor functions with similar aspect ratios and size-dependent spatial frequencies. A key finding is that the distribution of receptive-field sizes is scale invariant over a wide range of values, so there is no characteristic receptive field size selected by natural image statistics. The Gabor function aspect ratio is found to be approximately conserved by the learning rules and is therefore not well determined by natural image statistics. This allows for three distinct solutions: a basis of Gabor functions with sharp orientation resolution at the expense of spatial-frequency resolution, a basis of Gabor functions with sharp spatial-frequency resolution at the expense of orientation resolution, or a basis with unit aspect ratio. Arbitrary mixtures of all three cases are also possible. Two parameters controlling the shape of the marginal distributions in a probabilistic generative model fully account for all three solutions. The best-performing probabilistic generative model for sparse coding applications is found to be a gaussian copula with Pareto marginal probability density functions.


2013 ◽  
Vol 19 (7) ◽  
pp. 1228-1241 ◽  
Author(s):  
Hui Fang ◽  
G. K-L Tam ◽  
R. Borgo ◽  
A. J. Aubrey ◽  
P. W. Grant ◽  
...  

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