Efficient coding of natural scenes improves neural system identification
Fig 5
Past encoding or future prediction strategies using 3D shared filters perform equally well.
a. Top row: Responses of three exemplary GCL cells to 5-Hz noise stimulus (gray) and predictions of best performing models on test data (black, SI; blue, SI with PCA filters; red solid, hybrid for encoding the past; red dotted, hybrid for predicting the future). Bottom row: Respective learned RFs of the three cells (visualized by SVD). b. Mean model performance based on test data for SI, SI-PCA (128 bases), hybrid-natural-past, and hybrid-natural-future (both w = 0.4; n = 10 random seeds; p < 0.0001 for SI vs. hybrid-natural-past, p = 0.0005 for SI-PCA vs. hybrid-natural-past, p = 0.2563 for hybrid-natural-past vs. hybrid-natural-future, two-sided permutation test, n = 10, 000 repeats). c. Representative shared spatial and temporal filters of 3D models (n = 1 random seed, visualized by SVD; temporal kernels for UV and green stimulus channels indicated by purple and green, respectively). d. Mean R-squared of fitting a 2D Gaussian to shared spatial filters (for green stimulus channel; n = 10 random seeds per model; p = 0.0003 for SI vs. hybrid-natural-past, p = 0.4356 for SI-PCA vs. hybrid-natural-past, p = 0.1895 for hybrid-natural-past vs. hybrid-natural-future, two-sided permutation test, n = 10,000 repeats). Error bars in (b),(d) represent 2.5 and 97.5 percentiles with bootstrapping.