Associative conditioning remaps odor representations and modifies inhibition in a higher olfactory brain area

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Abstract

Intelligent behavior involves associations between high-dimensional sensory representations and behaviorally relevant qualities such as valence. Learning of associations involves plasticity of excitatory connectivity, but it remains poorly understood how information flow is reorganized in networks and how inhibition contributes to this process. We trained adult zebrafish in an appetitive odor discrimination task and analyzed odor representations in a specific compartment of the posterior zone of the dorsal telencephalon (Dp), the homolog of mammalian olfactory cortex. Associative conditioning enhanced responses with a preference for the positively conditioned odor. Moreover, conditioning systematically remapped odor representations along an axis in coding space that represented attractiveness (valence). Interindividual variations in this mapping predicted variations in behavioral odor preference. Photoinhibition of interneurons resulted in specific modifications of odor representations that mirrored effects of conditioning and reduced experience-dependent, interindividual variations in odor–valence mapping. These results reveal an individualized odor-to-valence map that is shaped by inhibition and reorganized during learning.

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Fig. 1: Odor representations in dpDp in NAV fish.
Fig. 2: Innate odor preference and associative olfactory conditioning.
Fig. 3: Learning- and experience-dependent enhancement of odor responses in dpDp.
Fig. 4: Experience strengthens pairwise correlations and modifies neuronal population activity in dpDp.
Fig. 5: Mapping odor space onto a valence axis.
Fig. 6: Experience-dependent enhancement of inhibition in dpDp.
Fig. 7: Nonuniform effects of inhibition in dpDp.
Fig. 8: Inhibition and reorganization of coding space.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

All codes used in this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

We thank A. Wanner for custom-made scripts for microscope control, E. Arn Bouldoires for excellent technical assistance and N. Temiz for help during behavioral training. We are grateful to K. Deisseroth (Stanford University, CA, USA) for DNA constructs containing eNpHR3.0YFP and to R. Köster (Technical University Braunschweig, Germany) for DNA constructs containing the 5xUAS cassette. We thank G. Keller, A. Lüthi, C. Meissner-Bernard and P. Rupprecht for critical comments on the manuscript and members of the Friedrich group for helpful discussions. This work was supported by the Novartis Research Foundation, the Swiss National Science Foundation (grant nos. 31003A_135196 and 310030B_1528331), fellowships from HFSPO (no. LT000278/2012-L) and EMBO (no. ALTF 994-2010) to T.F., and the European Research Council under the European Union’s Horizon 2020 research and innovation (grant no. 742576).

Author information

T.F. conceived the project, designed experiments, generated Tg fish lines, performed all experiments except for behavioral conditioning, analyzed data, interpreted data and wrote the manuscript. N.R.M. performed and analyzed behavioral conditioning experiments and commented on the manuscript. C.S. performed and analyzed behavioral conditioning experiments and commented on the manuscript. S.H. generated Tg fish lines and commented on the manuscript. R.W.F. conceived the project, designed experiments, interpreted data and wrote the manuscript.

Correspondence to Thomas Frank or Rainer W. Friedrich.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks David Schoppik and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1 Odor identity classification in dpDp (related to Fig. 1).

a. Classification of odor identity by template matching of activity vectors in naïve (NAV) fish using Pearson correlation distance (1 – Pearson correlation; cf. Fig. 1g). Fills show percentage of correctly decoded odors using a fixed population size of 95 neurons. White dashed line indicates chance level (25 %; McNemar test, n = 156 trials, d.f. = 1, Χ2 = 75.01, P < 10–16); black dashed line shows mean classification success, averaged over all four odors (McNemar test for comparison against 100% correct, Χ2 = 38.03, P = 7 x 10–10). Comparison against classification success using cosine distance (Fig. 1g; McNemar test): Χ2 = 0, P = 1. b. Same odor classification procedure using Euclidean distance as distance metric. Comparison against chance level (25 %; McNemar test): Χ2 = 69.01, P = 1 x 10–16. Comparison against 100% correct (McNemar test): Χ2 = 44.02, P = 3 x 10–11. Comparison against classification success using cosine distance (Fig. 1g; McNemar test): Χ2 = 4.17, P = 0.04. n and d.f. as in (a). ***P < 0.001.

Supplementary Figure 2 Innate odor preference and associative olfactory conditioning (related to Fig. 2).

a. Behavioral setup to analyze innate behavioral responses to odors. Tank water (control) or odor solution (His, Ser, Ala or Trp) were delivered to one side of the tank using a gravity-fed system. b. Experimental paradigm. Following acclimatization, tank water was applied to test for non-specific responses, followed 10 min later by odor application at the same location. Swimming speed was quantified before applications and for 40 s after each application. c. Left: Behavioral discrimination score (CS+ preference score, calculated as ζCS+ζCS– over the last nine trials) did not differ significantly between ALA, TRP, and HIS training groups (one-way ANOVA, d.f. = 42, F = 0.72, P = 0.49, N (ALA) = 12 animals, N (TRP) = 16, N (HIS) = 15). Open circles represent individual fish. Multiple comparisons between all groups (Tukey test, two-sided): ALA vs TRP, q = 0.21, P = 0.98; ALA vs HIS, q = 1.27, P = 0.65; TRP vs HIS, q = 1.59, P = 0.50. Right: behavioral preference for Ala vs Trp or His (Trp for ALA, TRP, UNC; His for HIS; ζAlaζTrp or His; 0: no preference; > 0: preference for Ala; < 0: preference for Trp or His). Ala preference score (calculated over last nine trials) differed between training groups (one-way ANOVA, d.f. = 54, F = 31.83, P = 9 x 10–12, N as before and N (UNC) = 12). Multiple comparisons between all groups (Tukey test, two-sided): ALA vs TRP, q = 12.66, P = 5 x 10–11; ALA vs HIS, q = 10.97, P = 2 x 10–9; ALA vs UNC, q =5.64, P = 0.001; TRP vs HIS, q = 1.63, P = 0.66; TRP vs UNC, q = 6.64, P = 0.0001; HIS vs UNC, q = 5.04, P = 0.004. Box plot: center line, median; box limits, interquartile range; and whiskers, s.d. d. Examples of behavioral responses during appetitive conditioning. Single trial examples of swimming trajectories during the 30 s after odor onset, but prior to food delivery (ALA fish). Trials of the same fish were chosen from the first (left panel) and last (right panel) training day. Top: trajectory plots of one CS+ trial (Ala, red) and one CS trial (Trp, blue). Brightness encodes z-level in the water column. Center and bottom: histograms of fish position in each video frame extracted from the trajectories above. e. Same plots as in (d) for a TRP fish. f. Mean learning curves for individual components of appetitive behavior (cf. Fig. 2d and ref.33). Lines and shading show the mean (± s.e.m.) of ζ for the first three days of training (nine trials per day). Comparisons between CS+ and CS (Wilcoxon signed rank test, two-sided, N = 43 animals, ALA, TRP, and HIS). z-level: day 1, T = 293, P = 0.03; day 2, T = 169, P = 0.0001; day 3, T = 111, P = 3 x 10–6. Speed: day 1, T = 291, P = 0.03; day 2, T = 85, P = 3 x 10–7; day 3, T = 121, P =6 x 10–6. Distance: day 1, T = 268, P = 0.01; day 2, T = 208, P = 0.001; day 3, T = 232, P = 0.003. Surface (peaks): day 1, T = 454, P = 0.82; day 2, T = 243, P = 0.005; day 3, T = 56, P = 1 x 10–8. Area: day 1, T = 271, P = 0.01; day 2, T = 297, P = 0.03; day 3, T = 199, P = 0.0007. Circling: day 1, T = 423, P = 0.55; day 2, T = 464, P = 0.92; day 3, T = 404, P = 0.41. ns: P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

Supplementary Figure 3 Experience modifies odor responses in dpDp (related to Figs. 3, 4).

a. Odor-specific response amplitudes expressed as percentage of mean amplitude in NAV fish. Lines and shading show the mean (± s.e.m.). Kruskal-Wallis tests followed by non-parametric multiple comparisons against NAV fish, two-sided: Ala-evoked responses (n = 5654, d.f. = 3, H = 92.04, P < 10–15): ALA, Q = −8.18, P < 10–15, n = 1591; TRP, Q = −2.03, P = 0.09, n = 1839; UNC, Q = 0.005, P = 1, n = 962. Trp-evoked responses (H = 140.48, P < 10–15): ALA, Q = −5.94, P = 6 x 10–9; TRP, Q = −4.77, P = 4 x 10–6; UNC, Q = 5.00, P = 1 x 10–6. His-evoked responses (H = 164.41, P < 10–15): ALA, Q = −11.53, P < 10–15; TRP, Q = −7.38, P = 3 x 10–13; UNC, Q = −1.35, P = 0.36. Ser-evoked responses (H = 142.20, P < 10–15): ALA, Q = −4.11, P = 8 x 10–5; TRP, Q = −6.83, P = 2 x 10–11; UNC, Q = 4.57, P = 1 x 10–5. Number of neurons (n) and d.f. as in Fig. 3a. Pairwise comparisons of Ala- vs Trp-evoked responses (paired t test, two-sided, d.f. = n−1): ALA, t = 4.37, P = 1 x 10–5; TRP, t = −2.81, P = 0.005; UNC, t = 5.78, P = 1 x 10–8. b. Signal-to-background ratio (SBR). Single neuron example illustrating background (grey) and signal (red) analysis windows. c. The SBR ratio of responses to Ala and Trp or His differed significantly between experimental groups (Ala/Trp: Kruskal-Wallis test with NAV, ALA, TRP, UNC: n = 2556, d.f. = 3, H = 21.66, P = 8 x 10–5; Ala/His: Wilcoxon-Mann-Whitney test, two-sided: NAV, n = 174, HIS, n = 357, U = 34’751, P = 0.03). Non-parametric multiple comparisons against NAV fish (n = 536), two-sided, for Ala vs Trp: ALA, Q = 0.67, P = 0.83, n = 809; TRP, Q = 3.50, P = 0.0009, n = 780; UNC, Q = −1.34, P = 0.36, n = 431. Analysis included only neurons that responded to both Ala and the second CS (Trp or His). Box plot: center line, median; box limits, interquartile range; and whiskers, s.d. d. Response amplitudes in NAV fish differed between crossings (Wilcoxon-Mann-Whitney test, two-sided: NAV #1, n = 1262 neurons (N = 9 animals); NAV #2, n = 528 neurons (N = 4 animals): U = 303’041, P = 0.003). Box plot: as in (c). e. Relative response amplitudes to familiar odors (Ala vs Trp) were not correlated to behavioral odor preference in individual UNC fish. Pearson correlation: r = −0.21 (N = 12 animals, t test of null hypothesis of r = 0, two-sided, d.f. = 10, t = −0.71, P = 0.49). Kendall‘s rank correlation: τ = 0.00 (test of null hypothesis of τ = 0 using a normal approximation, two-sided, z = 0.0, P = 1). f. Correlation of spontaneous activity traces was increased after associative conditioning and odor exposure both in weakly responding neurons (left; mean odor-evoked response amplitude: ∆F/F ≤ 10%; Kruskal-Wallis test, n = 7655, d.f. = 4, H = 331.76, P < 10–15) and in neurons with stronger odor responses (right; mean amplitude: 10 % < ∆F/F < 40%; Kruskal-Wallis test, n = 1302, d.f. = 4, H = 64.25, P = 2 x 10–13). Non-parametric multiple comparisons in weakly responding neurons, n (NAV) = 1573, two-sided: ALA, Q = –14.35, P < 10–15, n = 1240; TRP, Q = –11.04, P < 10–15, n = 1780; HIS, Q = –4.36, P = 3 x 10–5, n = 1807; UNC, Q = –13.86, P < 10–15, n = 1255. Non-parametric multiple comparisons in neurons with stronger responses, n (NAV) = 207, two-sided: ALA, Q = –6.04, P = 3 x 10–9, n = 341; TRP, Q = –4.38, P = 2 x 10–5, n = 374; HIS, Q = –2.97, P = 0.006, n = 230; UNC, Q = –7.31, P = 5 x 10–13, n = 150. Box plot: as in (c). g. Correlation of odor tuning curves (signal correlation) was increased after associative conditioning and odor exposure, both in weakly responding neurons (left; Kruskal-Wallis test, n = 4871, d.f. = 4, H = 264.53, P < 10–15) and neurons with stronger odor responses (right; Kruskal-Wallis test, n = 1297, d.f. = 4, H = 107.65, P < 10–15). Non-parametric multiple comparisons in weakly responding neurons, n (NAV) = 1105, two-sided: ALA, Q = –13.50, P < 10–15, n = 976; TRP, Q = –13.99, P < 10–15, n = 1221; HIS, Q = –10.31, P < 10–15, n = 762; UNC, Q = –6.76, P = 2 x 10–11, n = 807. Non-parametric multiple comparisons in neurons with stronger responses, n (NAV) = 207, two-sided: ALA, Q = –5.20, P = 4 x 10–7, n = 339; TRP, Q = –9.16, P = 2 x 10–10, n = 374; HIS, Q = –6.44, P < 10–15, n = 227; UNC, Q = –8.46, P < 10–15, n = 150. Only neurons that responded to at least one odor were included in this analysis. Box plot: as in (c). ns: P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

Supplementary Figure 4 Mapping odor space onto valence axis (related to Fig. 5).

a. Analysis of coding structures with dimensions representing cosine distance between task-relevant categories. (NAV, ALA, and UNC fish: CS+ was Ala, CS Trp, Neutral (Neu)1 was Ser, and Neu2 His; TRP: CS+ was Trp, CS Ala, Neu1 was His, and Neu2 Ser; HIS: CS+ was His, CS Ala, Neu1 was Trp, and Neu2 Ser; modifying the grouping of odors into these categories, for example by swapping the identities of the two neutral odors, had little effect on the analysis results). Throughout, number of animals and d.f. as in Fig. 4d. Far left: projection of coding structures onto the first two principal components. Each plot symbol represents one fish. Colors show association of each coding structure with the respective experimental group. For ALA, TRP, and HIS fish, larger marker size indicates higher behavioral discrimination score. Quantification for PC 1 in center panel. Left: PC 1 loadings on the six coding structure dimensions: PC 1 neither is dominated by a single task-relevant category nor reflecting the global distance between all category pairs. Center: Projection onto PC 1 was modulated by experience (Kruskal-Wallis test, H = 9.82, P = 0.04). Non-parametric multiple comparisons against NAV fish: ALA, Q = 0.26, P = 1; TRP, Q = 2.43, P = 0.03; HIS, Q = 2.29, P = 0.04; UNC, Q = 1.31, P = 0.38. Right: PC 1 score was correlated to behavioral odor preference in individual UNC fish. Pearson correlation: r = 0.72 (N = 12 animals, t test of null hypothesis of r = 0, two-sided, d.f. = 10, t = 3.26, P = 0.009). Kendall‘s rank correlation: τ = 0.52 (test of null hypothesis of τ = 0 using a normal approximation, two-sided, z = 2.33, P = 0.02). Far right: The correlation between PC 1 score and behavioral odor preference remained significant across all training groups. Pearson correlation: r = 0.52 (N = 55 animals, d.f. = 53, t = 4.44, P = 4 x 10–5). Kendall‘s rank correlation: τ = 0.35 (z = 3.78, P = 0.0002). Box plot: center line, median; box limits, interquartile range; and whiskers, s.d. b. Analysis of coding structures with dimensions representing standardized cosine distance between odor pairs (equivalent to Pearson correlation) of activity patterns (population vectors). Throughout, conventions, number of animals (N), statistical tests, and d.f. as in (a). Projection onto PC 1 was modulated by experience (Kruskal-Wallis test, H = 9.90, P = 0.04). Center: Associative conditioning in TRP fish altered the PC 1 score (non-parametric multiple comparisons against NAV fish): ALA, Q = −0.67, P = 0.90; TRP, Q = −2.42, P = 0.03; HIS, Q = −1.85, P = 0.13; UNC, Q = −2.59, P = 0.02. Right: PC 1 score was correlated to behavioral odor preference in individual UNC fish. Pearson correlation: r = −0.68 (t = −2.94, P = 0.01). Kendall‘s rank correlation: τ = 0.52 (z = 2.33, P = 0.02). Far right: The correlation between PC 1 score and behavioral odor preference remained significant across all training groups. Pearson correlation: r = −0.34 (t = −2.65, P = 0.01). Kendall‘s rank correlation: τ = 0.20 (z = −2.17, P = 0.03). Box plot: as in (a). c. Shuffling rows (observations, fish) before PCA reduced the variance explained by PC 1 to 27.5 ± 1.1 % (mean ± s.d.); bootstrap test, two-sided: P = 0.005 (left panel) and abolished the correlation between PC 1 and behavior. Pearson correlation: r = 0.00 ± 0.14; mean ± s.d.; bootstrap test, two-sided: P = 0.005 (right panel). Grey vertical bars indicate mean of 10’000 shuffling repetitions, red bars indicate mean of data. d. Shuffling columns (coding structure dimensions) before PCA also reduced the variance explained by PC 1 to 55.9 ± 0.6 % (mean ± s.d.); bootstrap test, two-sided: P = 0.005 (left panel) and reduced the correlation between PC 1 and behavior. Pearson correlation: r = −0.50 ± 0.01; mean ± s.d.; bootstrap test, two-sided: P = 0.01 (right panel). Vertical bars as in panel (c). e. Schematic model of learning-induced changes of odor representations in dpDp (ALA and TRP fish). Cube depicts a low-dimensional representation of olfactory coding space in dpDp, with the primary dimension being an axis representing valence (‘good’ to ‘bad’). Different odors are represented as filled circles, and reinforced odors as larger circles. In ALA fish, the main effect of learning was an enhancement of responses, in particular of the CS+ relative to the CS. As Ala is innately appetitive, reinforcing this odor does not result in a (major) reorganization along the valence axis (‘minor remapping’). In TRP (and HIS) fish, positive valence is assigned to a previously neutral odor and Ala is ‘devalued’ because it is not paired with food, resulting in a ‘major remapping’ of odor representations along the valence axis. Remapping of trained odors also changes representations of related odors (Fig. 5c, Supplementary Fig. 4a). f. Variance explained by PC 1 as a function of coding structure dimensions in a dataset from NAV fish with a larger number of odors (3154 neurons from 15 animals; eight odors: Phe, Trp, Met, Lys [10−5 M each], a mixture Phe-Met, a mixture Trp-Lys, a mixture of three bile acids [glycocholic acid, taurocholic acid, taurodeoxycholic acid; 10−6 M each], and a food extract). Given eight stimuli, the maximum dimensionality of coding structures is 28. We selected random subsets of dimensions (with replacement) and performed PCA to determine the fraction of variance represented by PC 1 as a function of the number of dimensions. Lines and shadings show median and 95% confidence intervals. Results are consistent with assumption that fraction of variance saturates with increasing dimensionality. g. Correlation between scores on the first four PCs and behavioral odor preference. Each plot shows the relationship between one PC and behavioral odor preference scores for all fish tested in behavioral experiments. A significant correlation to behavioral odor preference was observed only for PC 1 (throughout (g), number of animals as in Fig. 5, and statistical tests as in (a)). PC 1: cf. Fig. 5. PC 2: Pearson correlation: r = 0.11 (t = 0.81, P = 0.42). Kendall‘s rank correlation: τ = −0.02 (z = −0.25, P = 0.80). PC 3: Pearson correlation: r = −0.24 (t = −1.78, P = 0.08). Kendall‘s rank correlation: τ = −0.05 (z = −0.50, P = 0.62). PC 4: Pearson correlation: r = −0.06 (t = −0.42, P = 0.67). Kendall‘s rank correlation: τ = 0.03 (z = 0.28, P = 0.78). ns: P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001. Additional Text. PCA was performed using different data matrices as inputs. In all cases, the dimension of the input matrix was 136 x 6. Columns represented cosine distances between odor-evoked activity patterns, rows represented N = 68 different animals and two different conditions (control, PIN). Very similar results were obtained when PCA was performed independently for each photoinhibition condition (matrices with dimension 68 x 6). The following data matrices were examined: (1) Columns represented odor pairs defined by odor identity (His:Ser, His:Ala, Ser:Ala, His:Trp, Ser:Trp, Ala:Trp; cf. Fig. 5). (2) Columns represented odor pairs defined by task relevance (CS+:CS, CS+:Neutral1, CS:Neutral1, CS+:Neutral2, CS:Neutral2, Neutral1:Neutral2; cf. Supplementary Fig. 4a). (3) Columns represented odor pairs defined by odor identity, calculated as cosine distance between standardized activity patterns (standardization refers to subtraction of the vector mean, followed by division by the vector’s standard deviation). This is equivalent to using correlation distance (1 − Pearson correlation) as a distance metric (Supplementary Fig. 4b). (4) Columns were defined as in (1) and rows (observations) were shuffled (Supplementary Fig. 4c). (5) Columns were defined as in (1) and columns (variables) were shuffled (Supplementary Fig. 4a). When coding structures represented distances between defined odor pairs (1), PC 1 represented 62% of the variance and the Pearson correlation between the PC 1 score and behavioral odor preference was 0.52 (P = 4 x 10−5). When the coding space represented distances between task-relevant odor pairs (2), the variance represented by PC 1 was lower (57%) while the correlation between PC 1 score and behavioral odor preference remained almost unchanged (r = 0.52; P = 5 x 10−5). When fish (observations) were shuffled across experimental groups (4; (c)), or when distances (variables) were shuffled in each fish (5; (d); 10,000 shufflings each), the variance represented by PC 1 and the correlation between PC 1 score and behavioral odor preference decreased significantly. These observations show that experience-dependent modifications of coding structures depend systematically on the odor and task because shuffling of observations and variables both decreased the explained variance and the correlation to behavioral odor preference. However, when distances were shuffled independently in each fish (5), the explained variance remained relatively high and the correlation between the PC 1 score and behavioral odor preference remained significant. When fish were shuffled across groups (4), in contrast, the explained variance decreased substantially and the correlation between the PC 1 score and behavioral odor preference approached zero. These observations lead to the conclusion that modifications of coding structures in different experimental groups include a ‘common mode’ represented by PC 1. The magnitude of this mode was large when associative conditioning resulted in large odor-value reassignments (TRP, HIS) but very small when odor-value reassignments were minor (ALA). Uncoupled odor exposure resulted in a small recruitment of this mode, possibly reflecting a devaluation of Ala, although this effect was not statistically significant. Major modifications of coding structures therefore occurred along an axis (PC 1) that was closely related to valence.

Supplementary Figure 5 Prediction of odor preference across experimental groups (related to Fig. 5).

a. To confirm that coding structures undergo consistent changes along an axis representing valence we tested whether experience-dependent changes in coding structures can predict odor preference across experimental groups. We defined two subsets of fish: (1) the reference group, which consisted of naïve fish and one experimental group (for example, NAV (N = 13 animals) and ALA (N = 12)), and (2) the test group, which contained all other fish (for example, TRP (N = 16), HIS (N = 15), UNC (N = 12)). We then projected coding structures of the test group onto the PC 1 extracted from the reference group and asked whether the projections (‘test scores’) were correlated to behavioral odor preference in the test group. In all cases, correlations between test scores and behavioral odor preference were high and statistically significant. Hence, PC 1 extracted from all animals in any experimental group (and all NAV fish) defined a direction in coding space that reliably predicted behavioral odor preferences in the other, remaining experimental groups. These results directly demonstrate that different odor-reward assignments, as well as uncoupled odor exposure, resulted in modifications of coding structures along an axis that consistently represented valence (attractiveness). Pearson correlation coefficient (r) is reported (P values were determined using a t test of the null hypothesis of r = 0, two-sided, with d.f. = N–2). b. Same analysis as in (a) but the reference group contained NAV fish and two experimental groups (for example, NAV, HIS, UNC) while the test group contained the other two experimental groups (for example, ALA, TRP). As in (a), statistically significant correlations between test scores and behavioral odor preference were observed in all cases (correlations and statistical tests as in (a)). Number of animals in each group as in (a). c. Same analysis as in (a) but ALA fish were excluded from all analyses. The influence of ALA fish on the results was examined because Ala is an innately attractive odor. Excluding ALA modified correlations only minimally (compare to corresponding plots in b). Hence, results can be generalized in this dataset independently of the innate valence of the CS+. Number of animals in each group, correlations, and statistical tests as in (a). d. Correlation between PC 1 and behavioral odor preference when odor responses to Ala were excluded from the analysis in all experimental groups. Excluding Ala as an odor stimulus reduced the number of coding structure dimensions from six to three. Nevertheless, the correlation between PC 1 and behavioral odor preference remained highly significant (left) and the mapping of coding structures onto the first two PCs (right) was similar to the mapping under control conditions (Fig. 5). Hence, a consistent axis representing valence was identified by PCA even when responses to the innately attractive odor Ala were not considered. Number of animals in each group, correlations, and statistical tests as in (a).

Supplementary Figure 6 Role of inhibition in reorganization of neuronal representations (related to Figs. 6, 7, and 8).

a. Location of dpDp (violet). Green square depicts approximate location of images in Fig. 6a. b. Ca2+ signals evoked by PIN in the absence of odor stimulation, averaged over all neurons of all fish from the first crossing (NAV: N = 9; ALA: N = 12; TRP: N = 13; UNC: N = 8). Orange bar depicts light exposure; shading shows s.e.m.. The PIN-evoked amplitude increase was increased after associative conditioning or uncoupled odor exposure, consistent with the effect of PIN on odor-evoked responses (Fig. 6f). c. In the absence of halorhodopsin expression (UAS:NpHR; Tg(UAS:eNpHR3.0YFP), no Gal4 driver), orange laser light had no detectable effect on the mean odor-evoked activity. Graph shows odor-evoked Ca2+ signal averaged over all neurons, trials and odors under control conditions (black; n = 720, N = 2 animals) and during illumination (orange). Shading shows s.e.m.. Red bar indicates approximate duration of odor stimulation; orange bar depicts light exposure. We also observed no obvious effects on the structure of odor-evoked population activity or on spontaneous activity (not shown). d. PIN reduced the signal-to-background ratio (SBR). SBR was averaged over neuron-odor pairs involving neurons that responded to at least one odor (all fish from the same crossing; NAV: N = 9 animals; ALA: N = 12; TRP: N = 13; UNC: N = 8). Mean and standard deviation of background activity were estimated in separate trials without odor application (Methods). PIN vs control comparisons (paired t test, two-sided, d.f. = n−1): NAV, t = 12.42, P = 5 x 10–33, n = 969 neurons; ALA, t = 24.15, P = 8 x 10–108, n = 1384; TRP: t = 18.87, P = 2 x 10–71, n = 1521; UNC, t = 16.46, P = 4 x 10–52, n = 744. Under control conditions, SBR was increased after associative conditioning and uncoupled odor exposure (Kruskal-Wallis test, n = 4618, d.f. = 3, H = 67.29, P = 1 x 10−14). Non-parametric multiple comparisons against NAV fish, two-sided: ALA, Q = –6.19, P = 1 x 10–9; TRP, Q = – 7.61, P = 5 x 10–14; UNC, Q = –2.55, P = 0.02. Black dotted line: median of NAV fish (control). During PIN, SBR was significantly reduced in ALA and UNC fish (Kruskal-Wallis test, H = 58.20, P = 1 x 10−12). Non-parametric multiple comparisons against NAV fish, two-sided: ALA, Q = 4.49, P < 1x10–5; TRP, Q = – 0.06, P = 1; UNC, Q = 5.56, P = 5 x 10–8. Orange dotted line: median of NAV fish (PIN). Box plot: center line, median; box limits, interquartile range; and whiskers, s.d. e. PIN abolished differences in mean pattern distances between experimental groups. Mean pairwise cosine distance of activity patterns (‘pattern separation’) did not differ between experimental groups during PIN (Kruskal-Wallis test, H = 6.89, P = 0.15). Non-parametric multiple comparisons against NAV: ALA, Q = 0.44, P = 0.98; TRP, Q = –1.90, P = 0.11; HIS, Q = –0.28, P = 1; UNC, Q = –0.96, P = 0.67. Number of animals and d.f. as in Fig. 4d. Box plot: as in (d). f. PIN abolished differences in the structure of distance matrices between NAV fish and TRP or HIS fish (cf. Fig. 4e) but had opposing effects in UNC fish (Kruskal-Wallis test, H = 15.35, P = 0.003). Non-parametric multiple comparisons against NAV: ALA, Q = 0.08, P = 1; TRP, Q = –1.18, P = 0.47; HIS, Q = –0.20, P = 1; UNC, Q = –3.24, P = 0.002. Number of animals and d.f. as in Fig. 4d. Box plot: as in (d). g. Projection of coding structures onto the first two principal components during PIN. Colors show association of each coding structure with the experimental group. For ALA, TRP, and HIS fish, larger marker size indicates higher behavioral discrimination score. Distances between coding structures from individual fish (plot symbols) from different experimental groups were reduced as compared to control (Fig. 5b). Number of animals as in Fig. 5b. h. PIN-induced change of PC 2 score as a function of PC 2 score under control conditions. As for PC 1 (Fig. 8e), the effect of PIN was significantly correlated to the control PC 2 score across all animals (Pearson correlation): r = –0.77 (N = 68, t test of null hypothesis of r = 0, two-sided, d.f. = 66, t = –9.77, P = 2 x 10–14). Kendall‘s rank correlation: τ = –0.58 (test of null hypothesis of τ = 0 using a normal approximation, two-sided, z = –6.96, P = 3 x 10–12). Significant negative correlations were also observed in all individual groups except NAV (Pearson correlation, statistical tests as for all fish): NAV, r = –0.50, t = –1.93, P = 0.08; ALA, r = –0.87, t = –5.57, P = 0.0002; TRP, r = –0.80, t = –4.92, P = 0.0002; HIS, r = –0.87, t = –6.40, P = 2 x 10–5; UNC, r = –0.62, t = –2.50 P = 0.03. Kendall‘s rank correlation: NAV, τ = –0.33, z = –1.59, P = 0.11; ALA, τ = –0.82, z = –3.70, P = 0.0002; TRP, τ = –0.67, z = –3.60, P = 0.0003; HIS, τ = –0.77, z = –4.00, P = 6 x 10−5; UNC, τ = –0.55, z = –2.47, P = 0.01. Number of animals as in Fig. 4d. i. PIN abolished differences in PC 1 score between NAV, and TRP and HIS fish (Fig. 5d; Kruskal-Wallis test, H = 6.69, P = 0.16; same data as in h). Non-parametric multiple comparisons against NAV (PIN): ALA, Q = −0.31, P = 0.99; TRP, Q = 1.94, P = 0.11; HIS, Q = 0.27, P = 1; UNC, Q = 1.02, P = 0.62. Number of animals and d.f. as in Fig. 4d. In all experimental groups except NAV, PIN modulated the PC 1 score (PIN vs control comparisons, Wilcoxon signed rank test, two-sided): NAV, T = 21, P = 0.09; ALA, T = 0, P = 0.0004; TRP, T = 18, P = 0.008; HIS, T = 2, P = 0.0002; UNC, T = 11, P = 0.03). Box plot: as in (d). j. PIN decreased the correlation between PC 1 score and behavioral odor preference in UNC fish. Pearson correlation: r = 0.48 (N = 12, t test of null hypothesis of r = 0, two-sided, d.f. = 10, t = 1.72, P = 0.11). Kendall‘s rank correlation: τ = 0.33 (test of null hypothesis of τ = 0 using a normal approximation, two-sided, z = 1.51, P = 0.13). Orange line shows linear fit; dashed grey line shows linear fit to data obtained under control conditions (Fig. 5e). k. PIN decreased the correlation between PC 1 score and behavioral odor preference across all training groups. Pearson correlation: r = 0.35 (N = 55, t test of null hypothesis of r = 0, two-sided, d.f. = 53, t = 2.72, P = 0.009). Kendall‘s rank correlation: τ = 0.22 (test of null hypothesis of τ = 0 using a normal approximation, two-sided, z = 2.36, P = 0.02). Orange line shows linear fit; dashed grey line shows linear fit to data obtained under control conditions (Fig. 5f). ns: P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001. Dc: central zone of the dorsal telencephalon; Dl: lateral zone of the dorsal telencephalon; OB: olfactory bulb; TeO: optic tectum; V: ventral telencephalon. M: medial; P: posterior.

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