Sparse models for visual image reconstruction from fMRI activity

Statistical model is essential for constraint-free visual image reconstruction, as it may overfit training data and have poor generalization. In this study, we investigate the sparsity of the distributed patterns of visual representation and introduce a suitable sparse model for the visual image reconstruction experiment. We use elastic net regularization to model the sparsity of the distributed patterns for local decoder training. We also investigate the relationship between the sparsity of the visual representation and sparse models with different parameters. Our experimental results demonstrate that the sparsity needed by visual reconstruction models differs from the sparsest one, and the l2-norm regularization introduced in the EN model improves not only the robustness of the model but also the generalization performance of the learning results. We therefore conclude that the sparse learning model for visual image reconstruction should reflect the spasity of visual perceptual experience, and have a solution with high but not the highest sparsity, and some robustness as well.