Before revision:  We consider using these learned dictionaries as the front-end that can effectively imposing priors on the constituent components. 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:  Extensive experimental results also indicate that these interpretable kernels provide important information on the component features of a neuron's receptive field. In addition, we find 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) .