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Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images

Figure 10

Mean prediction of the linear one-layer (l), compressive nonlinear one-layer (cn) and nonlinear two-layer (2) SC and GWP models across the voxels that survived the threshold of 0.1 in the case of (2).

The mean prediction of the linear one-layer models were below the threshold of 0.1. The mean prediction of the nonlinear SC models were significantly better than those of the nonlinear GWP models. The compressive nonlinearity and the nonlinear second layer increased the mean prediction of the linear and compressive nonlinear models, respectively. The nonlinear second layer increased the mean prediction of the compressive nonlinear SC model more than it increased that of the compressive nonlinear GWP model. The error bars show 1 SEM across the voxels (bootstrapping method).

Figure 10

doi: https://doi.org/10.1371/journal.pcbi.1003724.g010