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Neural dynamics underlying associative learning in the dorsal and ventral hippocampus

Nature Neuroscience volume 26, pages 798–809 (2023)Cite this article

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Abstract

Animals associate cues with outcomes and update these associations as new information is presented. This requires the hippocampus, yet how hippocampal neurons track changes in cue–outcome associations remains unclear. Using two-photon calcium imaging, we tracked the same dCA1 and vCA1 neurons across days to determine how responses evolve across phases of odor–outcome learning. Initially, odors elicited robust responses in dCA1, whereas, in vCA1, odor responses primarily emerged after learning and embedded information about the paired outcome. Population activity in both regions rapidly reorganized with learning and then stabilized, storing learned odor representations for days, even after extinction or pairing with a different outcome. Additionally, we found stable, robust signals across CA1 when mice anticipated outcomes under behavioral control but not when mice anticipated an inescapable aversive outcome. These results show how the hippocampus encodes, stores and updates learned associations and illuminates the unique contributions of dorsal and ventral hippocampus.

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Fig. 1: Before conditioning, odor stimuli are more strongly represented in dCA1 versus vCA1.
Fig. 2: Discrimination training enhances task representations.
Fig. 3: Learned odor representations are sensitive to extinction but can be rapidly reinstated.
Fig. 4: Task representations stabilize with learning.
Fig. 5: Individual odor representations dominate dCA1, whereas vCA1 incorporates information about future outcome. Both regions represent anticipated outcome during the trace period.
Fig. 6: Aversive conditioning and reversal learning.
Fig. 7: Instrumental control of outcomes increases task-related representations in associative learning.

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Data availability

All source data can be downloaded from the Kheirbek laboratory GitHub site (https://github.com/mkheirbek).

Code availability

The analysis code supporting this study is available from the Kheirbek laboratory GitHub site (https://github.com/mkheirbek).

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Acknowledgements

We thank V. Namboodiri and L. Frank for discussions and comments and K. Litke, S. Chien, A. Vittala, A. Garg and C. Lacefield for technical assistance. J.S.B. was supported by the Brain and Behavioral Research Foundation (NARSAD) and the Sandler PBBR Independent Postdoctoral Fellow Research Award. M.A.L. was supported by a National Science Foundation Graduate Research Fellowship. M.A.K. was supported by the National Institute of Mental Health (R01 MH108623, R01 MH111754 and R01 MH117961); the National Institute on Deafness and Other Communication Disorders (R01 DC019813); a One Mind Rising Star Award; a research grant from the Human Frontier Science Program (RGY0072/2019); the Esther A. and Joseph Klingenstein Fund; the Pew Charitable Trusts; the McKnight Memory and Cognitive Disorders Award; and the Ray and Dagmar Dolby Family Fund.

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Author notes

  1. These authors contributed equally: Jeremy S. Biane, Max A. Ladow.

Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA

    Jeremy S. Biane, Sayi P. Boddu, Austin Fan, Shazreh Hassan, Naz Dundar, Daniel L. Apodaca-Montano, Lexi Zichen Zhou, Varya Fayner & Mazen A. Kheirbek

  2. Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Max A. Ladow, Naz Dundar, Nicholas I. Woods & Mazen A. Kheirbek

  3. Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA

    Fabio Stefanini

  4. Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, USA

    Mazen A. Kheirbek

  5. Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    Mazen A. Kheirbek

  6. Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA, USA

    Mazen A. Kheirbek

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Contributions

J.S.B., M.A.L. and M.A.K. conceived the project, designed the experiments and edited the paper. J.S.B. drafted the paper. J.S.B., M.A.L., N.I.W. and M.A.K. designed the experimental approaches. J.S.B., M.A.L., F.S. and M.A.K. designed the analysis methods. J.S.B., M.A.L. and F.S. wrote the analysis code. J.S.B., M.A.L., S.P.B., A.F., S.H., N.D., D.L.A.-M., L.Z. and V.F. performed data pre-processing. J.S.B., M.A.L., S.P.B. and A.F. performed surgeries. J.S.B. and M.A.L. performed two-photon imaging. J.S.B. and M.A.L. ran behavioral training experiments. J.S.B., S.P.B., A.F., S.H., N.D. and D.L.A.-M. performed histological analysis.

Corresponding authors

Correspondence to Jeremy S. Biane or Mazen A. Kheirbek.

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The authors declare no competing interests.

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Nature Neuroscience thanks Edward Nieh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Contact for reagents and resource sharing Further information and requests for resources and reagents should be directed to M.A.K.

Extended data

Extended Data Fig. 1 Implant localization and pre-training neural activity.

a, b. Reconstructed GRIN lens implant locations for all vCA1 (A) and dCA1 (B) animals used in odor-based studies. Colored lines indicate the estimated location of the lens impression left on the tissue. Atlas images adapted from78. c. Time course of odor presence at the nose cone. d. Cross-validated neural activity during the Pre session. Each trial type (odor1 or odor2) was separated into odd and even trials, and vCA1 neural activity was z-scored. For each time bin, z-scores were averaged across all trial subsets, and sorted by peak firing rate latency during odd trials. Line is population mean, shading is ±SEM. e. same as d, but for dCA1.

Extended Data Fig. 2 Population decoding of odor presentations prior to training.

a, b. Decoding confusion matrices. Actual trial type is on y-axis, trial type predicted by classifier is denoted by x-axis. Odor delivery period = 0–2 s; trace period = 2–4 s; sucrose delivery = 4 s (CS+trials only). c, d. Decoding trial type when using different time bin durations over which cell activity is averaged. Regardless of time bin duration used, dCA1 shows significantly higher decoding accuracy than vCA1 both during and soon after odor presentation. (n = 10 decoding iterations, n-matched 454 cells from 11 vCA1 and 5 dCA1 mice, two-sided Mann-Whitney U, color coded bars indicate p < 0.01). e. Odor-period decoding. Population activity during the last second of odor delivery was used to decode odor 1 or odor 2 from baseline. (n = 10 decoding iterations, n-matched 454 cells from 11 vCA1 and 5 dCA1 mice, two-sided Mann-Whitney U, error bars mean ± SEM, ** p < 0.01, *** p < 0.001). See Supplementary Table 1 for all statistical analysis details.

Extended Data Fig. 3 Learning-related changes in neural activity.

a. (left) Mean z-scored fluorescent signals for all recorded cells during the Early session, ordered by peak time bin. See Fig. 2e, f for Late session. (right) Line is mean, shading is ±SEM). b. Cross-validated neural activity. Each trial type (CS+ or CS-) was separated into odd and even trials, and neural activity was z-scored. For each time bin, z-scores were averaged across all trial subsets, and sorted by peak firing rate latency during odd trials. Population mean is shown directly below heatmap (line is mean, shading is ±SEM). c. Linear regression of lick rates and Ca2+ in vCA1 and dCA1 during Early and Late associative learning sessions (see Methods). We found that neural activity is not significantly correlated to lick rates (n = 11 vCA1 and 5 dCA1 mice, unpaired two-sided t-test, p > 0.05, error bars are mean ± SEM). d. Proportion of responsive cells of the total population whose activity was significantly modulated during odor- or trace-period compared to pre-odor baseline. Fisher’s exact test. Statistical power for the pre-training session (Pre) was too low for meaningful analysis (only 15 trials/trial-type in Pre vs 60 trials/trail-type in Early and Late). (n’s denoted on graph, two sided Fisher’s exact test, ** p < 0.01, *** p < 0.001,) See Supplementary Table 1 for all statistical analysis details.

Extended Data Fig. 4 Learning-related changes in population decoding.

a. Relationship between trial-type decoding accuracy and total number of cells (line is mean, shading is ±SD). b. Trial-type decoding accuracy for individual animals during the Late session. (n = 11 vCA1 mice, 5 dCA1, two-sided Mann-Whitney U test vs chance, error bars mean ± SEM). c. Population-activity decoding accuracy for CS+ or CS- trials from baseline.(n = 10 decoding iterations from n-matched of 454 cells from 11 vCA1 and 5 dCA1 mice, two sided Mann-Whitney U test, color coded bar is p < 0.01, line is mean, shading is ±SD). d. Visualization of population activity pattern similarity for CS + and CS- trials via MDS dimensionality reduction. Dot plots show a sample MDS run, bar charts plot the average of 10 runs (n = 10 MDS iterations, two sided Mann-Whitney U test, error bars mean ± SEM). e. Sample cumulative licking during the trace period for CS+ and CS- trials from the Early and second day of learning. The Aha point, in this example at trial 20, represents the first moment the difference between the cumulative licking in CS+ and CS- trials exceeded the learning threshold (see Methods). f. Trial-type decoding accuracy during odor or trace periods using 30 CS+ and CS- trials before and after the Aha point. In vCA1, decoding accuracy significantly increases after the aha point for the odor and trace periods (n = 11 vCA1 5 dCA1 mice, two sided Mann-Whitney U test). Before the aha point, decoding during trace is not significantly different from chance (n = 11 vCA1 5 dCA1 mice, two-sided Wilcoxon test, p > 0.05). In dCA1, aha decoding does not significantly increase during the odor period and increases by a small but significant amount during trace period (n = 11 vCA1 5 dCA1 mice,two sided Mann-Whitney U test). dCA1 trace period decoding before the aha point is already significantly above chance (n = 11 vCA1 5 dCA1 mice, two-sided Wilcoxon test, p < 0.05). Error bars mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001. See Supplementary Table 1 for statistical analysis details.

Extended Data Fig. 5 Confusion matrices for extinction and reacquisition sessions.

Decoding confusion matrices for Extinction day 2 (a) and Reacquisition sessions (b).

Extended Data Fig. 6. Tracking Single-cell and population dynamics across training reveals stability of task encoding accompanies learning.

a, c. Activity during CS+ trials for neurons registered across specific session pairs. For each time bin, activity z-scores for each neuron were averaged across all trials within a session, and neurons were sorted by peak firing rate latency during the indicated session. b, d. Quantification of cells with increased responsiveness to different task epochs. Individual cells show high remapping of responsiveness to CS+ task epochs across Early and Late sessions, but increased stability from Late to Reacquisition. Proportion of cells responsive across two sessions was compared to the expected distribution of overlap based on the proportion of responsive cells in each individual session (n = 241 cells from 11 vCA1 mice and 337 cells from 4 dCA1 mice for Early vs Late and n = 253 cells from 10 vCA1 mice and 377 cells from 5 dCA1 mice for Late vs Reacquisition. Level of significance for 10,000 shufflings). e-h. Same as in a-d, but for CS- trials (n = 241 cells from 11 vCA1 mice and 337 cells from 4 dCA1 mice for Early vs Late and n = 253 cells from 10 vCA1 mice and 377 cells from 5 dCA1 mice for Late vs Reacquisition. i, j. Comparison of weights assigned to individual cells during decoding analysis; higher weight indicates greater importance for encoding2. As activity is correlated with assigned weight, we plotted weights values after regressing out the components explained by the activity. We find an increased correlation of weight values after learning (Late and Reacquisition) compared to initial training (Early/Late), supporting a stabilization of task representations accompanies learning. (n = 241 cells from 11 vCA1 mice and 337 cells from 4 dCA1 mice for Early vs Late and n = 253 cells from 10 vCA1 mice and 377 cells from 5 dCA1 mice for Late vs Reacquisition, linear least- squares regression.). k, l. Confusion matrices for across-session decoding. * p < 0.05, ** p < 0.01, *** p < 0.001. See Supplementary Table 1 for all statistical analysis details.

Extended Data Fig. 7 Confusion matrices for CS+ vs CS- trial type classification and breathing correlations.

a, b. Confusion matrices for CS+ vs CS- trial type classification. c. Breathing rate was not correlated with calcium event activity in either hippocampal region. Data points represent individual animals (n = 11 vCA1, 5 dCA1 imaging sessions, unpaired two-sided t-test, p > 0.05, error bars are mean ± SEM). Data taken from Late session. See Supplementary Table 1 for all statistical analysis details.

Extended Data Fig. 8 Task representations show increased stability with learning following a break in training.

a. In the 2-odor task, Late and Reacquisition sessions were separated by multiple extinction sessions. To assess how task representations may change across a similar time period, but with no additional task experience, following learning of the 4-odor task, mice were kept in their homecage and rerun on the learned task 4 days later (Post). b. Mean lick rate during the trace period for all animals (n = 8 vCA1, 5 dCA1 mice,, two sided Mann-Whitney U test, * p < 0.05, *** p < 0.001, error bars mean ± SEM). c, d. Trial-type and CS+ vs CS- decoding accuracies were similar for the Post session (shown here) compared to Late (Fig. 5c and Extended Data Fig. 7a, b; Analyses used 150 cells for each region). e.As in Late session, odor and outcome information were multiplexed in vCA1 during the odor delivery period, while outcome information was present in both vCA1 and dCA1 during trace (n = 10 decoding iterations from n-matched 150 cells from 8 vCA1 and 5 dCA1 mice, two sided Mann-Whitney U test, *** p < 0.001, error bars are mean ± SEM). f. Pearson’s correlation of activity patterns across time bins. g. Task representations showed greater stability once learned. Analyses used cells registered across all 3 sessions. (n = 10 decoding iterations from n-matched 100 cells from 8 vCA1 and 5 dCA1 mice, two sided Mann-Whitney U test, * p < 0.05, ** p < 0.01, *** p < 0.001, error bars are mean ± SEM). h. Same as in g, but decoding CS+ vs CS- across sessions. See Supplementary Table 1 for all statistical analysis details.

Extended Data Fig. 9 Odor ID and reward expectation representations remain stable across reversal learning, while shock anticipation signals fade.

a. Trial-type decoding accuracy. Rew = reward trial. Sh = shock trial. (n-matched pseudopopulation of 444 cells from 10 vCA1 and 3 dCA1 mice, line is mean and shading is ±SD). b. Change in odor-period (left) or trace-period (right) decoding accuracies for CS+ shock vs CS- trials from Early to Late sessions (±SEM). Statistics compare Early and Late sessions for a specific hippocampal region (Mann-Whitney U test). n = 10 decoding iterations from n-matched pseudopopulation of 444 cells from 10 vCA1 and 3 dCA1 mice, two sided Mann-Whitney U test, * p < 0.05, ** p < 0.01, *** p < 0.001, error bars are mean ± SEM). c. Same as in b but decoding CS+ reward from CS- trials. d, e. Confusion matrices for trial-type decoding accuracy during Early (upper) or Late (lower) sessions. f. Schematic illustrating trial-type decoding across reversal learning. g. Hypothetical results for decoding CS+ reward from CS+ shock trials across reversal learning (for this set of results, stable encoding of US identity across reversal is assumed). Because data classes were labeled with respect to the outcome of a trial, and not the odor identity, stable neural representations of odor identity will manifest as cross-session decoding accuracies that are below chance (middle graph). h. Actual results for decoding trial type across reversal learning. The below chance decoding accuracy for CS+ reward vs CS+ shock during the odor period indicates representations of odor identity dominate the population activity during this time. (n-matched pseudopopulation of 281 cells from 10 vCA1 and 3 dCA1 mice, line is mean and error bars are ±SD). i-j. Across-reversal odor ID decoding accuracy during the odor period (i) and trial type during trace period (j) (n-matched pseudopopulation of 281 cells from 10 vCA1 and 3 dCA1 mice, two sided Mann-Whitney U test, *** p < 0.001, error bars are mean ± SEM). See Supplementary Table 1 for all statistical analysis details.

Extended Data Fig. 10 Headfixed active avoidance task results.

a. Lick (top) and running (bottom) behavior from an example mouse during the first day of training. Trial number is color-coded, yellow to black. During the first day of training, the mouse had very few trials with suprathreshold running, leading to few rewards and numerous shock deliveries. Shock delivery resulted in rapid, transient running. Vertical grey bar = odor delivery period; vertical blue/red bar = time of sucrose/shock delivery onset (on applicable trials). Blue ticks = time point when running exceeded threshold. Green ticks denote trials where shock was delivered. Light blue trace = average running speed. Sh = shock odor trial. b. Same as in a, but Late session for the same mouse. c. Confusion matrices for Late session, suprathreshold trials, n = 340 cells from vCA1 and dCA1. d. Pairwise decoding for trial type. While active avoidance trials are well discriminated from rewarded trials, decoding accuracy was lower for AA vs CS- trials during the trace period (n-matched pseudopopulation of 340 cells from 8 vCA1 and 4 dCA1 mice, two sided Mann-Whitney U test, * p < 0.05, ** p < 0.01, *** p < 0.001, error bars are mean ± SEM). e. Running was not correlated with vCA1 neural activity, but was moderately correlated with dCA1 activity (±SEM, Mann-Whitney U test). Data are from Late session. (n = 11 vCA1, 5 dCA1 imaging sessions, unpaired two-sided t-test, *** p < 0.001, error bars are mean ± SEM). f. To further assess how running may have contributed to our results, we trained a linear classifier to decode high vs low speed running trials during time bins outside of the task (5–10 seconds post odor delivery). While running speed could be decoded above chance in both regions, decoding was relatively weak. Significance stars above individual bars report significance level versus 50% chance decoding accuracy (n = 5 time bins,, two sided Wilcoxon signed-rank test, * p < 0.05, *** p < 0.001 error bars are mean ± SEM). See Supplementary Table 1 for all statistical analysis details.

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Biane, J.S., Ladow, M.A., Stefanini, F. et al. Neural dynamics underlying associative learning in the dorsal and ventral hippocampus. Nat Neurosci 26, 798–809 (2023). https://doi.org/10.1038/s41593-023-01296-6

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