Efficient coding of natural scenes improves neural system identification
Fig 3
Neural encoding tasks benefit from natural scene statistics.
a. Region-of-interest (ROI) mask of one recording field in dorsal retina (left) and mean Ca2+ responses (black) of exemplary ROIs in response to 6 repeats of noise stimuli (single trials in gray). b. Three representative GCL cell responses (gray) to the noise stimulus (cf. Fig 2a, left), together with predictions of best performing models on test data (black, SI; red, hybrid w/ natural scenes as input to the EC path, i.e., InputEC), and learned spatio-temporal receptive fields (RFs) visualized by SVD. c. Model performance (linear correlation coefficient, CC; mean for n = 10 random seeds per model) based on validation data for hybrid model with natural scenes (red), with phase-scrambled scenes (brown), or with noise (magenta) as InputEC, and for different weights. Note that the correlation values for the validation data are relatively low because these predictions were calculated on a single-trial basis (Methods). d. Best performance (mean for n = 10 random seeds per model) based on test data for SI, SI-PCA (16 bases), SI-DCT (4 bases), hybrid-natural (w = 0.2), hybrid-pha-scr (w = 0.3) and hybrid-noise (w = 0.4; p < 0.0001 for SI vs. hybrid-natural, p = 0.0085 for SI-PCA vs. hybrid-natural, p = 0.0011 for hybrid-natural vs. hybrid-pha-scr, two-sided permutation test, n = 10, 000 repeats). e. Scatter plot for model predictions based on test data for hybrid-natural (w = 0.2) vs. SI at one random seed, with each dot representing one neuron. f. Representative spatial filters (shared convolutional filters) for hybrid models with different InputEC and different weights. Upper: with w = 0.5; lower: with optimal w (see (c)) for hybrid models. g. Mean R-squared of fitting a 2D Gaussian to spatial filters (cf. (f)), for hybrid model with natural scenes (red), with phase-scrambled scenes (brown), or with noise (magenta) as InputEC, and for different w (n = 10 random seeds per model). h. Representative spatial filters (shared convolutional filters) for SI, SI with PCA filters (16 bases) and SI with DCT filters (4 bases). i. Mean R-squared of fitting a 2D Gaussian to the spatial filters for one chromatic stimulus channel (green; n = 10 random seeds per model; p < 0.0001 for SI vs. hybrid-natural, p < 0.0001 for SI-PCA vs. hybrid-natural, p = 0.0074 for hybrid-natural vs. hybrid-pha-scr, two-sided permutation test, n = 10, 000 repeats). Error bars in (c),(d),(g),(i) represent 2.5 and 97.5 percentiles obtained from bootstrapping.