Article Figures & Data
Figures
- Figure 1.
An olfactometer design for stable odor presentations. A, A schematic of the design. Filtered air supply is split into three paths, (i) a stream that normally flows to the animal, (ii) a stream used for the sample odor, and (iii) a stream used for the test odor. Each odorizing stream has a pair of three-way valves. When one path is engaged for odor presentation, the air from the selected odor path passes through an odor canister before being directed toward the final valve (red highlight). Simultaneously, the other path diverts air directly toward the final valve (blue highlight), bypassing the odor canisters. B, Odors used in this paradigm. C, Four sample odor (O1) test odor (O2) delays used for assessing the olfactometer performance, which were randomly interleaved. D, Example photoionization detector signals for the four permutations of sample and test odors (average of 12 trials), color coded by the interval used as in C. E, Photoionization detector signals for the test odor overlaid for all intervals tested. F, Test odors were passed through blank odor canisters to test the level of cross-contamination at four delay intervals as in C. G, Photoionization detector signals during the test stimulus periods were expressed as a fraction of the sample odor signal level. Levels from individual trials are shown.
- Figure 2.
Go/No-Go olfactory delayed nonmatch-to-sample task with a 5 s delay. A, Illustration of the olfactory DNMS. On a given trial, two odors (Odor 1 and Odor 2) are presented. Rewarded trials are trials where two odors are not the same (nonmatch). Reward is 20 µl of water. B, Timeline of training. Behavioral training started 2 weeks after surgery for head plate implantation. C, Schematic showing the trial structure. Flashes of an LED indicate the trial start. Sample and test odors (O1 and O2, respectively) are presented with an interval (5 s for the initial training, and 12 s afterward). Water reward was delivered on all rewarded trials. Reward was delivered earlier when mice generated anticipatory licks earlier (range of possible reward times, 0.5–2.5 s from O2 offset). D, Lick raster for an example session, separated by nonmatch trials (left) versus match trials (right). E, Example peristimulus time histogram of licks separated by four trial types from a proficient mouse. F, Relationship between stray licks (licks during the sample-test odor interval) and accuracy of DNMS performance. Symbols indicate individual mice. G, Learning curves for 5 s delay (hollow symbols) and 12 s delay (stars) shown with respect to the trials from the start of training (block size, 50 trials; left) and with respect to days (right); n = 6 mice. Mean and SEM are indicated.
- Figure 3.
DNMS with a shorter interval is easier to learn. A, Two training designs are compared. One cohort of mice that started the training with sample-test odor interval of 1.7 s (green) was compared with those that started with 5 s interval (blue; same data as Fig. 2). B, Learning curves for the two cohorts, showing behavioral accuracy in auROC for for 1.7 s delay (filled circles), 5 s delay (hollow circles), and 12 s delay (stars). Color scheme as in A. Block size, 50 trials; n = 6 and 5 mice for initial training with 5 s and 1.7 s, respectively. C, Summary comparison of acquisition speeds in terms of number of trials (left) and number of days (right) taken to reach the criterion (auROC = 0.8); *p = 0.043 (left) and 0.036 (right), one-way ANOVA with post hoc Tukey–Kramer comparisons; n.s., p = 0.82 (left) and 0.80 (right). Mean and SEM are indicated. D, Relationship between stray licks (licks during the sample-test odor interval) and accuracy of DNMS performance. Pearson's r = −0.549, p = 4.73 × 10−6. Symbols correspond to individual mice. n.s. = not significant at the 0.05 level.
- Figure 4.
Effect of reward timing on DNMS acquisition. A, With conditional reward timing (green), the water reward was delivered earlier if mice licked earlier (possible reward onset ranged from 0.5 to 2.5 s after the odor 2 offset). With fixed reward delivery time (burgundy), water reward was delivered at 2.5 s after the odor 2 offset. B, Timeline of training for the two cohorts, with sample-test odor interval indicated inside the parentheses. C, Examples of lick timing (vertical lines) relative to odor 2 (duration 0.6 s) and water delivery (light blue). For the conditional reward time group (left), the timing of reward onset depended on lick timing. For the fixed reward onset (right), water timing was fixed regardless of licking behavior. D, Histograms of water onset times for the two groups, for days 1–3 of the training. E, DNMS learning curves for the two cohorts for 1.7 s (filled circles), 5 s (hollow circles), and 12 s (stars) intervals. Horizontal line indicates the criterion level (auROC = 0.8). F, Summary of learning speeds, measured by number of trials taken to reach criterion performance at sample-test odor interval of 1.7 s (left), 5 s (middle), and 12 s (right) for the two cohorts; p = 0.047, 0.068, 0.87 (two-sample t tests), respectively; n = 5 mice for conditional reward timing and 6 mice for fixed reward timing. G, Relationship between stray licks (licks during the sample-test odor interval) and accuracy of DNMS performance. Pearson's r = −0.483, p = 1.83 × 10−4. Symbols correspond to individual mice. Cond., Conditional. n.s. = not significant at the 0.05 level.
- Figure 5.
Olfactory stimulus interval affects the slope, whereas reward timing affects the intercept of learning curves. A, Slope of the logistic curve, derived from the fitted parameter, beta, for the three protocols used. Beta/4 describes the slope at the steepest point; p = 0.036, one-way ANOVA, followed by Tukey–Kramer post hoc comparisons (p = 0.1202, 0.0377, and 0.8662 for 1 vs 2, 2 vs 3, and 1 vs 3 comparisons). B, Intercept values for the fitted logistic curves; p = 0.0393, one-way ANOVA, followed by Tukey–Kramer post hoc comparisons (p = 0.7387, 0.1307, and 0.0405 for 1 vs 2, 2 vs 3, and 1 vs 3 comparisons). C, Initial accuracy when switching to longer sample-test interval, normalized by the last session performance; p = 0.0455 for equal performance at 5 s interval, one-way ANOVA; p = 0.0498 for equal performance at 12 s, one-way ANOVA with post hoc Tukey–Kramer comparisons; n = 6 mice, 5 mice, and 6 mice for cohorts 1, 2, and 3, respectively. Cond., Conditional. n.s. = not significant at the 0.05 level.
- Figure 6.
Interval-dependent performance and generalization. A, Once mice were trained to perform DNMS with a 12 s interval, they went through one session where the sample-test odor interval was randomly selected from 1.7 s, 5 s, 12 s, or 20 s on a given trial [DNMS (var.)]. B, Example trial order, where gray intensity indicates the sample-test odor interval duration. C, Lick raster, sorted by nonmatch (left) and match (right) trials and by the duration of sample-test odor intervals, with grayscale indicating the interval duration. White areas indicate the time of odor presentations. D, Accuracy for each interval used, for two cohorts, with colors corresponding to the cohorts described in A. Mean and SEM are indicated; n = 4 mice for each cohort. E, After recovery from surgery, naive mice went through habituation and training for olfactory DNMS with 1.7 s interodor interval and reward delivery fixed at 2.6 s after odor offset. Once trained, the mice performed 1–2 sessions of DNMS with variable interodor delays. F, Generalization to longer intervals. Accuracy of performance for the interodor intervals is indicated, expressed auROC; ** and * indicate statistically significant deviation using t test from auROC = 0.5 at the significance level of 0.01 and 0.05 with Bonferroni correction, respectively; p = 0.0007, 0.0003, 0.0013, 0.0032, and 0.0189 for 1.7 s, 5 s, 8 s, 10 s, and 20 s delay intervals, respectively; n = 7 mice.
- Figure 7.
Stimulus similarity and the generalizability. A, B, Generalization to new odor comparisons. A, Timeline of behavioral training. B, Odors used for the DNMS tasks, as listed in A. Mice underwent DNMS training with dissimilar odor pair (butyl acetate vs methyl salicylate; C/D vs C/D), similar odor pair (ethyl valerate vs methyl valerate; E/F vs E/F), and ethyl butyrate at two concentrations (G/H vs G/H). C, D, Assessing the similarity of olfactory bulb activity patterns evoked in anesthetized mice by odors used in the DNMS task. C, Raster plots for an example unit for all odors used. D, Correlation coefficients for pairs of odors used in the DNMS tasks. Each data point corresponds to the similarity of population activity for each recording site; n = 7 locations, 3 mice. E, Learning curves for the new odor pairs. Each data point represents the accuracy for a block of 30 trials; n = 4 mice. F, Same data as in E but aligned for the start of training for the three new odor pairs for easy comparison. G, Lick raster plots from an example mouse for the four new nonmatch pairs introduced in the multiodor DNMS stage. Color coding follows the scheme in B. H, Evolution of anticipatory licks over time for new nonmatch odors (left) and match pairs (right); n = 4 mice. Mean and SEM are indicated.
- Figure 8.
Behavioral output patterns depend on reward timing. A, Example lick raster plots from mice that went through different training paradigms. Left, a mouse that started with 5 s sample-test odor interval and conditional reward timing; middle, starting with a 1.7 s interval and conditional reward timing; right, starting with a 1.7 s interval and fixed reward timing. B, Evolution of stray licking during sample-test odor interval for the three cohorts (n = 6, 5, and 6 mice, respectively). Thick lines indicate mean; shadings indicate mean ± SEM. C, Illustration of reaction times; timings of first three licks after odor 2 onset are averaged and expressed relative to the onset of odor 2. D, Evolution of reaction time with training for the three cohorts indicated with the same color scheme as in A. Thin lines represent individual mice, thick lines represent average. E, Peristimulus time histograms for licks generated on match (unrewarded) trials were compared between cohorts that started with the 1.7 s delay but differed in the reward timing (n = 5 and 6 mice, respectively). Thick lines indicate mean; shading indicates mean ± SEM.
- Figure 9.
Interplay between reward timing, behavioral threshold, and the discriminability of sensory representations. A, A drift-diffusion model with one bound (blue line) to model the Go/No-Go behavior. Drift rates, µs+ and µs−, are the strengths of momentary evidence following the S+ (nonmatch) stimulus and S− (match) stimulus, respectively. At each time point, a fixed amount of noise is added and accumulated over time (sensory evidence). Reaction time is the time at which the sensory evidence crosses the bound. B, Histograms of reaction times for an example animal over five training days. Observed licks (correct licks are licks on S+ trials; false alarms are licks on S− trials) shown as bars. Simulated result using fitted parameters superimposed with lines (solid lines indicate S+ simulation; dotted lines indicate S− simulation). C, Estimated bounds for cohorts with fixed reward timing (burgundy) and conditional reward timing (green). Di, Estimated drift rates for the cohort with late, fixed reward timing. Rew., Reward. Dii, Estimated drift rates for two cohorts with conditional reward timing. E, Comparison of estimated bounds (top) and the ratio of drift rates (µs+/µs−, bottom) for the three cohorts from the last day of initial training. For decision bounds, p = 0.011 and 0.099 across reward timing and p = 0.43 for cohorts with same reward timing but different sample-test intervals (one-way ANOVA with post hoc Tukey–Kramer tests). For drift rates, p = 0.0183 and 0.0639 across reward timing, and p = 0.86 for cohorts with same reward timing (one-way ANOVA with post hoc Tukey–Kramer tests). Mean and SEM are indicated. n.s. = not significant at the 0.05 level.
- Figure 10.
Reward timing effect is likely general; the acquisition of fine olfactory discrimination task is also affected. A, Schematic of the behavioral paradigm. After habituation, head-fixed mice were trained to discriminate between EB and eugenol (Eug) mixtures that differed in the mixed ratios. After reaching the criterion level of performance (80% correct) on 80/20 versus 20/80 discrimination, the mice underwent training for 60/40 versus 40/60 mixture discrimination. B, Trial structure. For the early reward group, the water reward arrived 1.2 s after the onset of odor. For the late reward group, the latency to reward was 3.2 s. C, Lick raster plots from example mice for S+ and S− odors as indicated, for early reward group (top) and late reward group (bottom). D, Peristimulus time histogram of lick occurrences for the last training session for each task. Rel., relative to E, Evolution of lick onset timing for the two cohorts over training. Dotted lines indicate the reward arrival times for the two cohorts; n = 6 mice for each cohort. Mean and SEM are indicated. F, Learning curves for the two cohorts. Each data point corresponds to the average accuracy (auROC) for a block of 40 trials sliding every 20 trials. Mean ± SEM is indicated. G, Number of trials taken to reach the criterion level of accuracy (auROC = 0.8) for the initial acquisition; p = 0.014, two-sample t test; n = 6 mice in each cohort. Mean and SEM are indicated.
Tables
Location in article Test used Sample size Test statistic Exact p value Figure 2F Pearson correlation coefficient 112 Blocks from 6 mice r = −0.785 1.1731 × 10−24 Figure 3C (left) One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Two groups (6 mice and 5 mice) F = 4.26 0.0378 Figure 3C (right) One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Two groups (6 mice and 5 mice) F = 5.82 0.0157 Figure 3D Pearson correlation coefficient 61 Blocks from 5 mice r = −0.549 4.727 × 10−6 Fig. 4F (left) two-sample t test Two groups (5 mice and 6 mice) t = 2.3014 0.0469 Fig. 4F (middle) two-sample t test Two groups (5 mice and 6 mice) t = 2.0727 0.0681 Fig. 4F (right) two-sample t test Two groups (5 mice and 6 mice) t = −0.1736 0.8665 Figure 4I Pearson correlation coefficient 55 Blocks from 6 mice r = −0.483 1.833 × 10−4 Figure 5A One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Three groups (6 mice, 5 mice, 6 mice) F = 4.25 0.036 Figure 5B One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Three groups (6 mice, 5 mice, 6 mice) F = 4.12 0.0393 Figure 5C (left) One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Two groups (5 mice and 6 mice) F = 5.38 0.0455 Figure 5C (right) One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Three groups (6 mice, 5 mice, 6 mice) F = 3.75 0.0498 Figure 6F t Test with Bonferroni multiple-comparison correction Seven mice t Statistics (1.7 s, 5 s, 8 s, 12 s, 20 s) = 7.3560, 8.6679, 6.5347, 5.2867, 3.4159) 0.0007, 0.0003, 0.0013, 0.0032, 0.0189 Fig. 9E (top) One-way ANOVA with post hoc multiple comparisons (Tukey-Kramer) Three groups (6 mice, 5 mice, 6 mice) F = 6.0588 0.0127 Fig. 9E (bottom) One-way ANOVA with post hoc multiple comparisons (Tukey–Kramer) Three groups (6 mice, 5 mice, 6 mice) F = 5.5841 0.0165 Fig. 10F Two-sample t test Six mice in each cohort t = −2.9758 0.0139
