Article Figures & Data
Figures
- Figure 1.
Visual stimuli for the smallest (σ = 4; top) and the largest (σ = 64; bottom) noise level. On the left, the same frame of the motion sequence is shown. For the first frame, a number of dots are placed randomly on the screen, and in subsequent frames the dots are shifted in the preferred direction of the H1 cell (indicated by the arrows). As shown, each dot has its own brightness. Properties of a sample dot are shown on the right. The noise sequence applied to this dot during presentation was made up of randomly drawn brightness values drawn from a Gaussian distribution centered at the mean brightness (127 on the eight-bit scale) and a SD of σ = 4, 8, 16, 32, or 64, defining the noise level (shown for σ = 4 and 64). This distribution was truncated at mean ± 2σ. Although the brightness variations over time are quite small (top), modulations covering the full brightness range are possible at the largest noise level (σ = 64).
- Figure 4.
Averaged discrimination of reference and test responses as a function of the noise level added to the stimuli and the temporal resolution of the measure of discrimination as developed by Victor and Purpura (1997). Data are based on a set of 11 H1 neurons recorded in 11 different flies. Transects b and c are shown in more detail in b and c. b, Mean discrimination of reference and test responses at the maximum noise level (σ = 64) in dependence of the temporal resolution was used for data analysis. The error bars show the SD of percentage correct decisions across the 11 cells. The shaded area represents the domain of uncertainty (see Materials and Methods). c, As in b, but the noise amplitude was varied at the “best” temporal resolution (15.625 msec).
- Figure 5.
Mean discrimination performance as a function of the noise correlation and the temporal resolution of the measure of discrimination as developed by Victor and Purpura (1997). The experiments were performed at the maximum noise level (σ = 64). The average performance is based on 10 H1 recordings in another set of 10 different flies than those on which the data shown in Fig. 4 are based. The transections refer to sections shown in b-d. b, The average discrimination performance at 100% noise correlation expressed in percentage correct decisions as a function of the temporal resolution was used for data analysis. Error bars show the SD across the 10 H1 recordings. The shaded area shows the domain of uncertainty (see Materials and Methods). c, Same data as in b but the noise correlation was varied at the “best” temporal resolution. The temporal resolution was 15.625 msec. d, Same data as in c but at a temporal resolution used for data analysis of 500 msec. e, Mean discrimination performance at 100% noise correlation and a noise level of σ = 16 as a function of the temporal resolution for four H1 recordings of four different flies. The error bars represent the SD of performance levels. Shown in gray is the domain of uncertainty, which represents the performance levels that are likely to be the result of internal noise and photon noise but not of the added external noise.
- Figure 2.
Data analysis for a single cell example. a, The raster plots show 750 msec sections of 10 responses to reference (left) and test (right) stimulus at the maximum noise level (σ = 64). A common spike pattern can be seen in all responses despite differences in timing of individual spikes. Although all reference responses were elicited by the same stimulus, they do not seem to be more similar to each other compared with the test responses that were evoked by stimuli, which were statistically equivalent but differed in the noise sequence added on the stimuli. The way of analyzing more subtle changes in the temporal structure of the responses is sketched by the lines drawn between the highlighted reference response and all other reference and test responses: (1) each reference response is compared with all other reference responses (solid lines) with two different measures of similarity (see Materials and Methods), and the average similarity within the reference responses is calculated; (2) the mean similarity of each reference response to all test responses is determined (dashed lines), and the average similarity between reference and test responses is calculated. b, If the external noise affects the responses, the reference responses should be more similar to each other compared with the test responses. With this assumption, we tried to classify the individual reference responses on the basis of the similarity values estimated with the measure developed by Victor and Purpura (1997) (see Materials and Methods). The percentage of correct classifications is plotted as a function of added noise level for this single cell example. The shaded area represents the domain of uncertainty (see Materials and Methods). Discrimination performances falling into this range are likely to be a consequence of chance. A significant effect of the added noise on the responses can be assumed if the actual percentage correct value is outside the domain of uncertainty. c, Same data as in b but the responses were compared using the measure developed by Kretzberg et al. (2001).
- Figure 3.
Mean firing rate of H1 in response to reference and test stimuli at the five noise levels (σ = 4, 8, 16, 32, and 64) used in the experiments. The box and whisker diagrams represent the distribution of mean firing rates, averaged over all presentations of each stimulus type. All diagrams are based on the same 11 H1 neurons recorded in 11 different flies. The horizontal lines in the boxes represent the lower, median, and upper quartile of the data. The whiskers extending vertically from the boxes show the extent of the rest of the data. The distributions were tested for significant differences using a Wilcoxon's test for paired samples. An α value, shown above each pair of distributions, larger than 5% denotes a nonsignificant difference.
In this issue
