We then applied these maps to a feedforward model of orientation selectivity.
These directions may not necessarily remain the samethroughout the analysis and loading; hence, the modeling of orientation ofcrack plays a significant role on the response of the structure.
This changes the diffraction pattern which now does not accurately describe the prevailing model of orientation, creates the additional diffraction reflections, and is clearly elucidated by HRTEM.
As an example, Camperi and Wang [24] have proposed and studied a network model of visuo-spatial working memory in prefontal cortex adapted from the ring model of orientation of Ben-Yishai and colleagues [1].
A further motivation was the following studies of Bressloff and Cowan [4, 13, 14] where they study a spatial extension of the ring model of orientation of Ben-Yishai [1] and Hansel, Sompolinsky [2].
Recently, Harrison and Bex [3] presented "A Unifying Model of Orientation Crowding in Peripheral Vision", which elegantly explains these two characteristics of crowding with one unifying mechanism.
This equation is richer than the ring model of orientation as it contains an additional information on the contrast of the image in the orthogonal direction of the prefered orientation.
The embedding of the ring model of orientation in the structure tensor model can be explained by the intrinsic relation that exists between the two sets of matrices SPD ( 2, R ) and S + ( 1, 2 ).
Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections.
The Gabor response magnitude is the model of orientation for the selective neuron in the primary visual cortex [31] and is related to the local energy spectrum, while the Gabor phase can capture the information from the wavelet's zero crossing [2].
