Article Figures & Data
Figures
- Figure 1.
Typical RGC responses of the dataset D1 to flashed gratings of spatial frequency 37 mcpd and different phases. Colored ellipses superimposed on grating images show the estimated receptive fields of the chosen RGCs. For each RGC chosen, 105 repetitions recorded with the 4096 APS CMOS MEA are plotted from 0 s (stimulus onset) to 0.5 s. We found no RGC exhibiting a clear latency tuning to the grating phase. However, a clear modulation of the RGC spike count with the grating phase can be observed for some cells.
- Figure 2.
Latency variability. A–F, Considering stimulus 17 (φ = 0°, 37mcpd). A, The SD is plotted as a function of the mean latency over the 105 repetitions, for all 764 RGCs of the dataset D1, for (B) only the 147 OFF cells, for (C) only the 111 ON-OFF cells, or for (D) only the 506 ON cells (see Materials and Methods for the classification method). The black line corresponds to a SD that is equal to the mean latency. This shows the considerable variability of individual latencies. E, The probability distribution of the individual latency SD for all cells (black), OFF cells (red), ON-OFF cells (green), and ON cells (blue). F, The probability distribution of the SD of latency difference for all cell pairs (black), OFF cell pairs (red), ON-OFF cells pairs (green), and ON cell pairs (blue).
- Figure 3.
A, Partial information diagram for two variables, based on Williams and Beer (2010), their Figure 1. The two inner circles represent the mutual information between two variables, R1 and R2, considered separately, and a third variable S. Where they overlap is the redundant information; where they do not is the unique information provided by each. The outer ellipse represents the mutual information between the pair (R1,R2) and S. The area not covered by the inner circles is the synergistic information. Decomposition of the information using PID for (B) dataset D1 and (C) dataset D2. The histograms show the amount of redundant, unique, and synergistic information for the four different spatial frequencies (9, 18, 37, and 75 mcpd). Error bars show standard error on the mean (SEM), but due to the large number of pairs sampled they are too small to be visible.
- Figure 4.
Distance between WFS evoked by different stimuli. A, Confusion matrix showing the mean Spearman correlation coefficient ρ computed for all possible stimuli pairs, across all the trials of the dataset D1. It can be interpreted as a measure of how different are the ranks of the first stimulus-evoked spikes related to two different stimuli: ρ = 1 for identical ranked lists and ρ = −1 for opposite ranked lists. Periodic patterns appear, which can be related to phase differences. B, For each spatial frequency, the variations of ρ(0, φ)|{φ=45...315}, where φ are the other gratings differing with their phases, are plotted. Continuous lines stand for ρ computed across the trials. Dashed lines stand for ρ computed using shuffled trials. The more the phase changes, the more the ranked emitted spikes are different. Shuffling the trials decreases this modulation. Error bars show SEM. C, Quantification of the effect due to shuffling the trials observed in B as a relative difference between ρ(0,45) and ρ(0,180) in normal (Δ) and shuffled (Δs) condition (see Results for details). Shuffling the trials leads to a loss of ρ up to 30%. D, E, F, Same analysis as in A, B, C, using the dataset D3. Periodic variation of ρ as a function of the phase can be seen but not as clear as in dataset D1. Error bars show SEM.
- Figure 5.
Discrimination performance of the spike count, the latency, and the ROC decoders. The fraction of correct identifications is plotted as a function of the spatial frequency for the spike count code (black), the latency code (gray), the ROC with latencies (red), and the ROC with spike counts (blue). A, All 764 RGCs of the dataset D1 and (B) all the 649 RGCs of the dataset D2 were used in this analysis. Results show that all the decoders perform well in this task (close to 1, maximal value). The horizontal line indicates chance level. Shaded areas show SEM.
- Figure 6.
Discrimination performance as a function of the number of RGCs. The fraction of correct identifications is plotted for the spike count code (black), the latency code (gray), and the ROC with latencies (red), and the ROC with spike counts (blue), as a function of the number of neurons. A, Responses of the dataset D1 related to stimuli 9–16 (18 mcpd) are used in this analysis. From a population size of 30 RGCs and higher, the ROC with latencies tends to perform better than the latency decoder. B, Analysis on the responses of the dataset D2 related to the same stimuli as in A. The horizontal line indicates chance level. Shaded areas show SEM.
- Figure 7.
Discrimination performance as a function of the time window after the stimulus onset. The fraction of correct identifications is plotted for the spike count code (black), the latency code (gray), the ROC with latencies (red), and the ROC with spike counts (blue) as a function of the length of the observation window. This time window varied from 0.05 to 0.5 s after the stimulus onset. Responses of (A) the dataset D1 and (B) the dataset D2, related to stimuli 9–16 (18 mcpd) are used in this analysis. ROC with latencies decoder rapidly outperforms the latency decoder and reaches its maximal performance within 0.15 s after the stimulus onset. B, The curve of the ROC with spike counts is hidden by the ROC with latencies. The horizontal line indicates chance level. Shaded areas show SEM.
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