Organization of Neural Population Code in Mouse Visual System

Population decoding performance by visual area for six stimulus classes. A, Stimulus decoding accuracy for individual randomly subsampled populations consisting of 1, 2, 4, 8, 16, 32, 64, and 128 neurons (black dots, jittered for visual clarity) and curve fits (solid lines). The number of populations per area is listed in the titles (n). In all visual areas, the majority of small populations (one to four neurons) outperformed chance level (gray line at 16.67% accuracy). However, small population performance in VISrl was more concentrated near chance level than all other areas. Individual populations of 128 neurons achieved near-perfect accuracy in all visual areas except VISrl. B, Population averaged accuracy by visual area (solid lines) with standard error (shaded regions). The line colors correspond to the visual area indicated by the line colors in A. C, Statistically significant (p < 0.05) pairwise comparisons of decoding accuracy at 128 neurons between the six visual areas using Tukey’s test. VISrl underperforms all other visual areas.

Stimulus-specific population decoding. A, Visual area averaged stimulus-specific decoding accuracy similar to Figure 2. The line color corresponds to the stimulus indicated by the legend in C. High-performing areas with similar overall decoding accuracy show differential accuracy in predicting specific stimuli. B, Stimulus-specific decoding accuracy averaged across all populations. There is differential accuracy in decoding the specific stimulus classes, with some being harder to decode than others. C, Statistical significance map (same convention as Fig. 2C). The natural movies are significantly more difficult to decode than all other stimuli, and the locally sparse noise is significantly more difficult to decode than all others except the natural movies.

Population decoding of directions for the drifting grating epoch. A, Direction decoding accuracy (same conventions as Fig. 2). Note that in VISrl, small populations (one to two neurons) performed closer to chance level (gray line at 12.5% accuracy) than the same sized populations in other areas. B, Population averaged accuracy by visual area (solid lines) with SE (shaded regions). The line colors correspond to the visual area indicated by the line colors in A. C, Statistical significance map (same convention as Fig. 2C). Three high-performing areas (VISp, VISal, VISl) showing similar performance are anatomically adjacent. Similarly, two of three low-performing areas (VISpm and VISam) showing similar performance are anatomically adjacent. D, E, Mean orientation (D) and direction (E) selectivity index (with SEM) per area (see Materials and Methods).

Neural population is synergistically encoding directional information. Accuracy of correlation blind decoder (gray bars; independent decoder) is compared to the joint decoder (same value as in Fig. 4B) for the population size of 128 neurons. Statistical significance indicated by paired t test.

Population decoding performance by recording depth for six stimulus classes (same conventions as Fig. 2). On average, small populations (one to two neurons) performed better than chance level performance (gray line at 16.67% accuracy). The 325- to 350-µm group significantly underperforms two shallower groups (175 and 265–300 µm).

Population decoding performance by imaging depth for eight drifting grating directions (same conventions as Fig. 4). On average, small populations (one to two neurons) in the three high-performing depth groups (175, 265–300, and 365–435 µm) outperformed chance level (gray line at 12.5% accuracy), while small populations in the low-performing 325- to 350-µm group performed at chance level.

Comparison of results of linear SVM and MLR classification. SVM and MLR classification accuracy for subsampled populations of 1, 2, 4, 8, 16, 32, 64, and 128 neurons are represented by a single point. Similar results were achieved by both classifiers.