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

Figure 5

Receptive fields of the SC2 model.

The parameter tuning varied across the voxels and had a bias for high spatial frequencies and oblique orientations. (A) Two-dimensional Gaussian functions that were fit to the responses of three representative voxels to point stimuli at different locations. (B) Responses of three representative voxels to sine-wave gratings that spanned a range of orientations and spatial frequencies. (C) Mean responses across the voxels to sine-wave gratings that spanned a range of orientations and spatial frequencies.

Figure 5

doi: https://doi.org/10.1371/journal.pcbi.1003724.g005