Before revision:  We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
After revision:  We found that models with complex sparse code prior are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .