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Evolving schema representations in orbitofrontal ensembles during learning

Abstract

How do we learn about what to learn about? Specifically, how do the neural elements in our brain generalize what has been learned in one situation to recognize the common structure of—and speed learning in—other, similar situations? We know this happens because we become better at solving new problems—learning and deploying schemas1,2,3,4,5—through experience. However, we have little insight into this process. Here we show that using prior knowledge to facilitate learning is accompanied by the evolution of a neural schema in the orbitofrontal cortex. Single units were recorded from rats deploying a schema to learn a succession of odour-sequence problems. With learning, orbitofrontal cortex ensembles converged onto a low-dimensional neural code across both problems and subjects; this neural code represented the common structure of the problems and its evolution accelerated across their learning. These results demonstrate the formation and use of a schema in a prefrontal brain region to support a complex cognitive operation. Our results not only reveal a role for the orbitofrontal cortex in learning but also have implications for using ensemble analyses to tap into complex cognitive functions.

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Fig. 1: Task design and behaviour.
Fig. 2: Dimensionality of the task representation.
Fig. 3: Cross-problem decoding.
Fig. 4: Cross-subject decoding.
Fig. 5: Acceleration of behavioural and neural changes across problems.

Data availability

The dataset used in this study is available at https://doi.org/10.17605/OSF.IO/5MH4Y.

Code availability

The MATLAB code used in this study is available at https://doi.org/10.17605/OSF.IO/5MH4Y.

References

  1. Bartlett, F. C. Remembering: A Study in Experimental and Social Psychology (Cambridge Univ. Press, 1932).

  2. Piaget, J. Langage et Pensée Chez L'Enfant (Delachaux et Niestlé, 1923).

  3. van Kesteren, M. T., Ruiter, D. J., Fernández, G. & Henson, R. N. How schema and novelty augment memory formation. Trends Neurosci. 35, 211–219 (2012).

    Article  Google Scholar 

  4. Gilboa, A. & Marlatte, H. Neurobiology of schemas and schema-mediated memory. Trends Cogn. Sci. 21, 618–631 (2017).

    Article  Google Scholar 

  5. Tse, D. et al. Schemas and memory consolidation. Science 316, 76–82 (2007).

    Article  CAS  ADS  Google Scholar 

  6. Zhou, J. et al. Rat orbitofrontal ensemble activity contains multiplexed but dissociable representations of value and task structure in an odor sequence task. Curr. Biol. 29, 897–907.e3 (2019).

    Article  CAS  Google Scholar 

  7. Zhou, J. et al. Complementary task structure representations in hippocampus and orbitofrontal cortex during an odor sequence task. Curr. Biol. 29, 3402–3409.e3 (2019).

    Article  CAS  Google Scholar 

  8. Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A. & Miller, L. E. Long-term stability of cortical population dynamics underlying consistent behavior. Nat. Neurosci. 23, 260–270 (2020).

    Article  CAS  Google Scholar 

  9. Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).

    Article  ADS  Google Scholar 

  10. Baram, A. B., Muller, T. H., Nili, H., Garvert, M. & Behrens, T. E. Entorhinal and ventromedial prefrontal cortices abstract and generalise the structure of reinforcement learning problems. Preprint at https://doi.org/10.1101/827253 (2020).

  11. McKenzie, S. et al. Hippocampal representation of related and opposing memories develop within distinct, hierarchically organized neural schemas. Neuron 83, 202–215 (2014).

    Article  CAS  Google Scholar 

  12. McKenzie, S., Robinson, N. T., Herrera, L., Churchill, J. C. & Eichenbaum, H. Learning causes reorganization of neuronal firing patterns to represent related experiences within a hippocampal schema. J. Neurosci. 33, 10243–10256 (2013).

    Article  CAS  Google Scholar 

  13. Morrissey, M. D., Insel, N. & Takehara-Nishiuchi, K. Generalizable knowledge outweighs incidental details in prefrontal ensemble code over time. eLife 6, e22177 (2017).

    Article  Google Scholar 

  14. Rubin, A. et al. Revealing neural correlates of behavior without behavioral measurements. Nat. Commun. 10, 4745 (2019).

    Article  ADS  Google Scholar 

  15. Mack, M. L., Preston, A. R. & Love, B. C. Ventromedial prefrontal cortex compression during concept learning. Nat. Commun. 11, 46 (2020).

    Article  CAS  ADS  Google Scholar 

  16. Farovik, A. et al. Orbitofrontal cortex encodes memories within value-based schemas and represents contexts that guide memory retrieval. J. Neurosci. 35, 8333–8344 (2015).

    Article  CAS  Google Scholar 

  17. Jones, B. & Mishkin, M. Limbic lesions and the problem of stimulus–reinforcement associations. Exp. Neurol. 36, 362–377 (1972).

    Article  CAS  Google Scholar 

  18. Jones, J. L. et al. Orbitofrontal cortex supports behavior and learning using inferred but not cached values. Science 338, 953–956 (2012).

    Article  CAS  ADS  Google Scholar 

  19. Wimmer, G. E. & Shohamy, D. Preference by association: how memory mechanisms in the hippocampus bias decisions. Science 338, 270–273 (2012).

    Article  CAS  ADS  Google Scholar 

  20. Wilson, R. C., Takahashi, Y. K., Schoenbaum, G. & Niv, Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279 (2014).

    Article  CAS  Google Scholar 

  21. Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016).

    Article  CAS  ADS  Google Scholar 

  22. Schuck, N. W., Cai, M. B., Wilson, R. C. & Niv, Y. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016).

    Article  CAS  Google Scholar 

  23. Garvert, M. M., Dolan, R. J. & Behrens, T. E. A map of abstract relational knowledge in the human hippocampal-entorhinal cortex. eLife 6, e17086 (2017).

    Article  Google Scholar 

  24. Behrens, T. E. J. et al. What is a cognitive map? Oganizing knowledge for flexible behavior. Neuron 100, 490–509 (2018).

    Article  CAS  Google Scholar 

  25. Gardner, M. P. H. & Schoenbaum, G. The orbitofrontal cartographer. Preprint at https://doi.org/10.31234/osf.io/4mrxy (2020).

  26. Gallagher, M., McMahan, R. W. & Schoenbaum, G. Orbitofrontal cortex and representation of incentive value in associative learning. J. Neurosci. 19, 6610–6614 (1999).

    Article  CAS  Google Scholar 

  27. Takahashi, Y. K. et al. Neural estimates of imagined outcomes in the orbitofrontal cortex drive behavior and learning. Neuron 80, 507–518 (2013).

    Article  CAS  Google Scholar 

  28. Stalnaker, T. A., Cooch, N. K. & Schoenbaum, G. What the orbitofrontal cortex does not do. Nat. Neurosci. 18, 620–627 (2015).

    Article  CAS  Google Scholar 

  29. Schoenbaum, G., Nugent, S. L., Saddoris, M. P. & Setlow, B. Orbitofrontal lesions in rats impair reversal but not acquisition of go, no-go odor discriminations. Neuroreport 13, 885–890 (2002).

    Article  Google Scholar 

  30. Gardner, M. P. H., Conroy, J. S., Shaham, M. H., Styer, C. V. & Schoenbaum, G. Lateral orbitofrontal inactivation dissociates devaluation-sensitive behavior and economic choice. Neuron 96, 1192–1203.e4 (2017).

    Article  CAS  Google Scholar 

  31. Hirokawa, J., Vaughan, A., Masset, P., Ott, T. & Kepecs, A. Frontal cortex neuron types categorically encode single decision variables. Nature 576, 446–451 (2019).

    Article  CAS  Google Scholar 

  32. Nogueira, R. et al. Lateral orbitofrontal cortex anticipates choices and integrates prior with current information. Nat. Commun. 8, 14823 (2017).

    Article  CAS  ADS  Google Scholar 

  33. Young, J. J. & Shapiro, M. L. Dynamic coding of goal-directed paths by orbital prefrontal cortex. J. Neurosci. 31, 5989–6000 (2011).

    Article  CAS  Google Scholar 

  34. Calhoun, V. D., Adali, T., Pearlson, G. D. & Pekar, J. J. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151 (2001).

    Article  CAS  Google Scholar 

  35. Hyvärinen, A. & Oja, E. Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000).

    Article  Google Scholar 

  36. McKeown, M. J., Hansen, L. K. & Sejnowsk, T. J. Independent component analysis of functional MRI: what is signal and what is noise? Curr. Opin. Neurobiol. 13, 620–629 (2003).

    Article  CAS  Google Scholar 

  37. Wang, J. & Chang, C.-I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans. Geosci. Remote Sens. 44, 1586–1600 (2006).

    Article  ADS  Google Scholar 

  38. Bell, A. J. & Sejnowski, T. J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995).

    Article  CAS  Google Scholar 

  39. Long, Q. et al. Consistent run selection for independent component analysis: application to FMRI analysis. In IEEE International Conference on Acoustics, Speech and Signal Processing 2581–2585 (2018).

  40. Akhonda, M. A. B. S., Levin-Schwartz, Y., Bhinge, S., Calhoun, V. D. & Adali, T. Consecutive independence and correlation transform for multimodal fusion: application to EEG and FMRI Data. In IEEE International Conference on Acoustics, Speech and Signal Processing 2311–2315 (2018).

  41. Jia, C. et al. C–ICT for discovery of multiple associations in multimodal imaging data: application to fusion of fMRI and DTI data. In 53rd Annual Conference on Information Sciences and Systems 1–5 (2019).

  42. Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27 (2011).

    Article  Google Scholar 

  43. Zhang, Y. et al. Object decoding with attention in inferior temporal cortex. Proc. Natl Acad. Sci. USA 108, 8850–8855 (2011).

    Article  CAS  ADS  Google Scholar 

Download references

Acknowledgements

The authors thank the NIDA IRP histology core for technical assistance with histology. This work used the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). This work was supported by a grant from the NIDA (K99DA049888 to J.Z.) and the Intramural Research Program at NIDA (ZIA-DA000587 to G.S.). The opinions expressed in this article are the authors’ own and do not reflect the view of the NIH or DHHS.

Author information

Authors and Affiliations

Authors

Contributions

J.Z. and G.S. designed the experiments; J.Z. and M.M.-C. collected the data; J.Z. analysed the data with advice and technical assistance from C.J., M.P.H.G. and W.Z.; and J.Z. and G.S. wrote the manuscript with input from the other authors.

Corresponding authors

Correspondence to Jingfeng Zhou or Geoffrey Schoenbaum.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks Alison Preston and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Behavioural performance on each problem and of each rat.

a, b, The behavioural learning was assessed by percent of correct (%correct) on rewarded trials (‘Go’ trials; blue) and non-rewarded trials (‘No-Go’ trials; red) across training days. Each rat accomplished one session each day. The data was plotted for each problem across rats (a; n = 9 rats) or each rat across problems (b; n = 5 odour problems). The days shown on the x-axis are the actual training days. Rats showed stable behavioural performance after day 15 and not all rats finished 23 days of training. To align the learning process between problems for further data analyses, we truncated the learning on each odour problem to 15 sessions, consisting of data from the first 14 sessions of learning plus data from the session with the best performance thereafter. Note that the training day of the last sessions with the best performance was referred to as ‘day 15’, except in this figure. Data are presented as mean ± s.e.m. A two-way ANOVA was performed for each panel (Reward × Day; R × D). See Supplementary Table 8 for detailed statistics.

Extended Data Fig. 2 Reaction time and %correct during learning.

a, Reaction time on day 1 and day 15. The reaction time measured the time period from odour port exit (‘unpoke’) to water well entry (‘choice’). The data presented here only included correct rewarded trials and incorrect non-rewarded trials. Reaction times on trial types in sequence S1 are plotted upwards, and those on trial types in sequence S2 are plotted downwards. Darker colours highlight four trial types that require rats to remember and use past sequences to perform correctly. n = 37 and 36 sessions on day 1 and day 15, respectively. Data are presented as mean ± s.e.m. b, The changes of reaction time on correct rewarded trials and incorrect non-rewarded trials during learning. c, d, The changes of reaction time during learning on the two pairs of trial types (c: S2a4+ and S2b5+; d: S2b4- and S2a5-). e, %Correct on all non-rewarded trial types and two (S2b4- and S2a5-) that required the recall of odour sequences. f, Reaction time on all non-rewarded trial types and two (S2b4- and S2a5-) that required the recall of odour sequences. Only incorrect trials (rats making a ‘Go’ choice on non-rewarded trials) were included. bf, n = 37, 40, 40, 38, 38, 39, 38, 39, 39, 38, 39, 40, 36, 38, 36 sessions from day 1 to day 15. Data are presented as mean ± s.e.m. ad, Two-way ANOVAs (Trial Type × Day). e, f, Two-way ANOVAs (Problem × Day). See Supplementary Table 9 for detailed statistics.

Extended Data Fig. 3 Histology and single-unit analyses.

a, Red squares show the reconstructed recording sites (n = 18 recording sites from 9 rats). Two electrode bundles were implanted bilaterally in OFC of each rat. Each electrode bundle consisted of 16 single wires. b, The number of neurons recorded across days. c, Cumulative distribution of neurons that showed different firing rates to all the odour stimuli. d, The averaged firing rates of all the neurons to all the odour stimuli. One-way ANOVA with the factor of Day: F(14,16828) = 0.39, P = 0.98. See b and Supplementary Tables 1 and 2 for n = number of neurons on each day. Data are presented as mean ± s.e.m. e, The percent of neurons that were significantly selective to at least one of the 24 trial types (one-way ANOVA; p values were adjusted by the Benjamini-Hochberg procedure to control the false discovery rate; BH-FDR; P < 0.05 was used to determine if one neuron was significantly selective to 24 trial types). f, The percent of neurons that showed selectivity to reward vs. non-reward trials (one-way ANOVA; BH-FDR; P < 0.05 was used to determine if one neuron was significantly selective to the current value).

Extended Data Fig. 4 Changes of activity space during learning.

a, Using the classical multidimensional scaling (cMDS) to visualize the dissimilarity matrix shown in Fig. 2. The coloured numbers (1 – 6) indicate positions (P1 – P6). Light blue and light red: rewarded and non-rewarded positions in S1, respectively; dark blue and dark red: rewarded and non-rewarded positions in S2, respectively. b, Hierarchical clustering of 24 trial types based on population neural activities on day 1. The dissimilarity matrix in Fig. 2 was used to construct a hierarchical clustering tree by an unweighted average linkage method. The clustering results were shown in dendrograms. c, The MDS plots to visualize the dissimilarity matrix on day 15 with the same colour code as in a. d. The hierarchical clustering analysis on day 15. e, Averaged pair-wise distances between 24 trial types. One-way ANOVA (factor: Day): F(14,7485) = 2279.2, P = 0, n = 500 repeats. f, Averaged pair-wise distances within reward trial types. One-way ANOVA (factor: Day): F(14,7485) = 4.4 × 10−4, P = 0, n = 500 repeats. g, Averaged pair-wise distances within non-reward trial types. One-way ANOVA (factor: Day): F(14,7485) = 1.9 × 10−4, P = 0, n = 500 repeats. h, The ratio of averaged pair-wise distance between and within reward vs. non-reward trials. One-way ANOVA (factor: Day): F(14,7485) = 1.1 × 10−4, P = 0, n = 500 repeats. e–h, Data are presented as mean ± s.d. i, Percent of explained variance across linear discriminant components (LCs). j, Heatmap plots of variance distributions across LCs and training days. Warmer colour mean higher percent of variance explained by certain LC, and vice versa. Four panels were used with different coloured bar scales (from left to right: 0 – 1; 0 – 5; 0 – 10; 0 – 20) to better visualize the same result.

Extended Data Fig. 5 Obtaining training and test sets.

a, Simulation of manifold alignment with the canonical correlation analysis (CCA). Two sets of Gaussian signals (n = 4 for each set) were generated to represent two sets of neurons recorded from two respective task sessions. Correlations of paired neurons between sessions were controlled (r = 0.99, 0.9, 0.85, 0.01; Pearson correlation). The neurons between sessions were misaligned such that the correlations between paired neurons were low (shown in the left; P = 0.58, 0.52, 0.86, 0.35; Pearson correlation) to mimic the misalignment of neurons during experimental recording sessions. After the manifold alignment, the recovered neural components (that is, canonical components) were aligned (P = 1.8 × 10−94, 7.0 × 10−34, 3.5 × 10−27, 0.82; *P < 0.05 shown in blue; Pearson correlation). The aligned components (#1, #2, and #3) represent the generalized neural activity across sessions, while the non-correlated components (#4) represent session-specific neural activity. b, For cross-problem decoding, neurons recorded from different pairs of odour problems were separately subjected to dimensionality reduction. The resulted two matrices (480 trials × 30 principal components; PCs) were aligned through CCA to obtain two correlated matrices (U1 and U2 for training; U3 and U4 for testing; 480 trials × 30 CCs for each matrix), which were concatenated (480 trials × 60 CCs) for further use as either a training (U1U2) or test (U3U4) set. Since there were 5 odour problems in total, for each repeat, the left one was combined with one of the two problems for the training set. For cross-subject decoding, the 9 rats were randomly separated into 4 groups. Groups 1 and 2 were used to obtain the training set (U1U2), while Groups 3 and 4 were used to obtain the test set (U3U4).

Extended Data Fig. 6 Paired canonical components between problems.

a, b, This figure is an extension of Fig. 3a. Each panel plots one pair of canonical components (CCs), one from the training set and another from the test set, starting with CC4, the first CC not plotted in the main text figure. To obtain the training set, CCA was performed on a pair of problems (for example, problem 1 and problem 2) to identify commonalities in the aligned neural subspaces. Similarly, to obtain the test set, the CCA was performed on a different pair of problems (for example, problem 3 and problem 4). The scores of paired CCs (one from the training set and the other from the test set) were plotted against the 24 trial types for both day 1 (a) and day 15 (b). The 24 trial types are ordered as P1(S1a,S1b,S2a,S2b), P2(S1a,S1b,S2a,S2b), P3(S1a,S1b,S2a,S2b), P4(S1a,S1b,S2a,S2b), P5(S1a,S1b,S2a,S2b), P6(S1a,S1b,S2a,S2b). Blue circles indicate positive trial types, and red circles indicate negative trial types. Four trial types are highlighted with filled circles (S2a4, S2b4, S2a5, and S2b5, in this order). The r in each panel is the correlation coefficient between paired CCs from the training and test sets (n = 480 trials for each CC; Pearson correlation). Data are presented as mean ± s.d.

Extended Data Fig. 7 Cross-problem decoding of sequences and positions.

a, b, Cross-problem decoding of sequences S1a vs. S1b (a) and S2a vs. S2b (b) at six positions (P1 – P6). Training and test sets were generated with the same approach described in Extended Data Fig. 5b with all 480 trials. For decoding analysis in each panel, only particular trial types (for example, S1a1 vs. S1b1) were selected. A one-way ANOVA (factor: Day) was performed and shown in each panel. c, Confusion matrices of 6 positions (P1 – P6) as a result of the cross-problem decoding of these positions. Trial types within the same position (for example, S1a, S1b, S2a, and S2b) were lumped together. d, Cross-problem decoding of positions during learning. A one-way ANOVA was used to test the effect of learning: F(14,7485) = 85.4, P = 3 × 10−228. e, Confusion matrices of the first three positions (P1 – P3) as a result of the cross-problem decoding of these three positions. Note that at each one of these three positions, the current and surrounding reward availabilities across sequences are similar, while at following positions (P4 and P5), the reward availabilities in S2b are not consistent with those in other sequences (S1a, S1b, S2a). f, Cross-problem decoding of the first three positions during learning. A one-way ANOVA was used to test the effect of learning: F(14,7485) = 247.8, P = 0. a, b, d, f, The round markers indicate that the mean decoding accuracy exceeded the 95% confidence interval (CI) of decoding accuracy from the same decoding process but with shuffled trial-type labels. Dotted line: chance level. n = 500 repeats. Data are presented as mean ± s.d.

Extended Data Fig. 8 Paired canonical components between subjects.

a, b, This figure is an extension of Fig. 4a. Each panel plots one pair of CCs, one from the training set and another from the test set, starting with CC 4, the first CC not plotted in the main text figure. For the training set, the CCA was performed on two groups of rats (for example, rat group 1 and rat group 2) to identify commonalities in the aligned neural subspaces. For the test set, the CCA was performed on a different two groups of rats (for example, rat group 3 and rat group 4). The scores of paired CCs (one from the training set and another from the test set) were plotted against the 24 trial types for both day 1 (a) and day 15 (b). The 24 trial types are ordered as P1(S1a,S1b,S2a,S2b), P2(S1a,S1b,S2a,S2b), P3(S1a,S1b,S2a,S2b), P4(S1a,S1b,S2a,S2b), P5(S1a,S1b,S2a,S2b), P6(S1a,S1b,S2a,S2b). Blue circles indicate positive trial types, and red circles indicate negative trial types. Four trial types were highlighted with filled circles (S2a4, S2b4, S2a5, and S2b5, in this order). The r in each panel is the correlation coefficient between paired CCs from the training and sets (n = 480 trials for each CC; Pearson correlation). Data are presented as mean ± s.d.

Extended Data Fig. 9 Cross-subject decoding of sequences and positions.

a, b, Cross-subject decoding of sequences S1a vs. S1b (a) and S2a vs. S2b (b) at six positions (P1 – P6). Training and test sets were generated with the same approach described in Extended Data Fig. 5b with all 480 trials. For decoding analysis in each panel, only particular trial types (for example, S1a1 vs. S1b1) were selected. A one-way ANOVA (factor: Day) was performed and shown in each panel. c, Confusion matrices of 6 positions (P1 – P6) as a result of the cross-subject decoding of these positions. Trial types within the same position (for example, S1a, S1b, S2a, and S2b) were lumped together. d, Cross-subject decoding of positions during learning. A one-way ANOVA was used to test the effect of learning: F(14,7485) = 38.1, P = 3.7 × 10−101. e, Confusion matrices of the first three positions (P1 – P3) as a result of the cross-subject decoding of these three positions. Note that at each one of these three positions, the current and surrounding reward availabilities across sequences are similar, while at following positions (P4 and P5), the reward availabilities in S2b are not consistent with those in other sequences (S1a, S1b, S2a). f, Cross-subject decoding of the first three positions during learning. A one-way ANOVA was used to test the effect of learning: F(14,7485) = 110.16, P = 3.1 × 10−291. a, b, d, f, The round markers indicate that the mean decoding accuracy exceeded the 95% confidence interval (CI) of decoding accuracy from the same decoding process but with shuffled trial-type labels. Dotted line: chance level. n = 500 repeats. Data are presented as mean ± s.d.

Extended Data Fig. 10 Comparisons of poke latencies sorted by discounted future reward between day 1 and day 15.

a–d, Poke latency measuring the time from the onset of the houselights (‘light’) to nosepoke (‘poke’) at the odour port, sorted according to discounted future reward across a: sequence blocks 1 – 5; b, sequence blocks 6 – 10; c, sequence blocks 11 – 15; d, sequence blocks 16 – 20 on day 1 (grey lines) compared to the poke latencies averaged across all 20 sequence blocks on day 15 (black lines). Each sequence block has 24 trials and comes from 24 trial types. Analyses were performed on each odour problem separately (problem 1–5; from upper to lower panels). In each panel, r is the correlation coefficient between the grey and black lines and p is the p-value for the correction. Number of sessions (that is, rats) used in the analyses: problem 1 (n = 9 on day 1; n = 6 on day 15); problem 2 (n = 9 on day 1; n = 8 on day 15); problem 3 (n = 7 on day 1; n = 8 on day 15); problem 4 (n = 6 on day 1; n = 7 on day 15); problem 5 (n = 6 on day 1; n = 7 on day 15). Data are presented as mean ± s.e.m.

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Supplementary Tables

Number of recorded neurons (Tables 1 – 2) and detailed results for statistical testing in the main figures and Extended Data figures (Tables 3 – 9).

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Zhou, J., Jia, C., Montesinos-Cartagena, M. et al. Evolving schema representations in orbitofrontal ensembles during learning. Nature 590, 606–611 (2021). https://doi.org/10.1038/s41586-020-03061-2

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