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Past experience shapes the neural circuits recruited for future learning

Nature Neuroscience volume 24, pages 391–400 (2021)Cite this article

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Abstract

Experimental research controls for past experience, yet prior experience influences how we learn. Here, we tested whether we could recruit a neural population that usually encodes rewards to encode aversive events. Specifically, we found that GABAergic neurons in the lateral hypothalamus (LH) were not involved in learning about fear in naïve rats. However, if these rats had prior experience with rewards, LH GABAergic neurons became important for learning about fear. Interestingly, inhibition of these neurons paradoxically enhanced learning about neutral sensory information, regardless of prior experience, suggesting that LH GABAergic neurons normally oppose learning about irrelevant information. These experiments suggest that prior experience shapes the neural circuits recruited for future learning in a highly specific manner, reopening the neural boundaries we have drawn for learning of particular types of information from work in naïve subjects.

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Fig. 1: Inhibition of LH GABAergic neurons by infusion of a Cre-dependent AAV carrying NpHR into the LH of GAD-Cre rats.
Fig. 2: LH GABAergic neurons are necessary to encode fear memories after reward learning.
Fig. 3: LH GABAergic neurons are recruited to encode fear memories only in rats that experience contingencies between cues and rewards.
Fig. 4: Computational modeling of the fear-conditioning data supports a role for LH GABAergic neurons in learning about the shock-predictive cue after experience with rewards.
Fig. 5: LH GABAergic neurons oppose learning of cue–cue associations after reward learning.
Fig. 6: LH GABAergic neurons oppose learning of cue–cue associations in naïve rats.
Fig. 7: LH GABAergic neurons are necessary to downregulate processing of explicitly irrelevant cues.

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Data availability

The data that support the findings of this study, and any associated custom programs used for its acquisition, are available from the corresponding authors upon reasonable request.

Code availability

Simulations were performed using custom-written functions in MATLAB (MathWorks), available on GitHub at https://github.com/mphgardner/LH_Inact_Model/.

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Acknowledgements

The authors thank M. Morales and C. Mejias-Aponte for their assistance. This work was supported by grant R01-MH098861 (to G.S.), the Intramural Research Program at the National Institute on Drug Abuse (ZIA-DA000587 to G.S.) and a National Health and Medical Research Council CJ Martin Fellowship (to M.J.S.). The opinions expressed in this article are the authors’ own and do not reflect the views of the National Institutes of Health/Department of Health and Human Services.

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Authors and Affiliations

  1. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA

    Melissa J. Sharpe

  2. National Institute on Drug Abuse, Intramural Program, Baltimore, MD, USA

    Hannah M. Batchelor, Lauren E. Mueller, Matthew P. H. Gardner & Geoffrey Schoenbaum

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  1. Melissa J. Sharpe
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  4. Matthew P. H. Gardner
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Contributions

M.J.S. and G.S. designed the experiments. M.J.S., H.M.B. and L.E.M. collected the data. M.J.S. analyzed the data, and M.J.S. and M.P.H.G. conducted the modeling. M.J.S. and G.S. interpreted the data and wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Melissa J. Sharpe or Geoffrey Schoenbaum.

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The authors declare no competing interests.

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

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

Extended data

Extended Data Fig. 1 Histological verification of Cre-dependent NpHR and eYFP in GAD+ neurons and fiber placement in the LH for all experiments.

Top row: Unilateral representation of the bilateral viral expression in the LH, -2mm to -3mm posterior to bregma. Bottom row: Approximate location of fiber tips in LH, indicated by black squares, -2mm to -3mm posterior to bregma.

Extended Data Fig. 2 Rats in our NpHR learners group showed a persistent increase in conditioned fear to the contextual cues, which extinguished before tone presentations in the extinction test.

During conditioning, our NpHR learners group showed high levels of fear to the background contextual cues (left; see Fig. 3 in main text for more information). To reduce these levels of context fear before the test, 24 hours after conditioning, rats received a context extinction session where they were placed in the experimental chambers without any stimuli. Here, we found that our NpHR learners group maintained higher level of context fear relative to our eYFP learners group (middle). This context extinction was effective in reducing contextual fear as all rats showed low levels of freezing at the beginning of the next test session, where we presented the tone under extinction to examine fear that had acquired to these stimuli (right). A mixed-design repeated-measures ANOVA on levels of freezing to the contextual cues across the context and tone extinction sessions showed a main effect of time (F14,140 = 4.614, p = 0.000), and a significant session x time x group interaction (F14,140 = 1.697, p = 0.032). This interaction was owed to a between-group difference in freezing during context extinction that revealed itself most prominently towards the end of the scoring period (group: F1,10 = 5.939, p = 0.035), that was not seen in the tone extinction test (group: F1,10 = 0.007, p = 1.000). Further, there was a significant difference in freezing exhibited by the NpHR group when comparing the context extinction session with the tone test (n = 6 rats; F1,10 = 8.071, p = 0.018), that was not present in the eYFP control group (n = 6 rats; F1,10 = 1.161, p = 0.307). Finally, a one-way ANOVA showed there was no between-group difference in freezing to the context immediately before tone presentations in the tone test after context extinction had taken place (F1,10 = 1.943, p = 0.194). Error bars = SEM.

Extended Data Fig. 3 Responding during conditioning in the second-order conditioning experiment (see Fig. 4 main text).

Rates of responding are represented as time spent in the food port (%; ±SEM). Rats (n = 12 eYFP; n = 8 NpHR) learnt to distinguish between A2 and B2 during conditioning, with no difference in the rates of learning between groups (note: patch cords were placed on rats in session 6 of conditioning to habituate them to the cords prior to pairings of A1→A2 and B1→B2, which is why there is a dip in responding). A repeated-measures ANOVA revealed a main effect of stimulus (F1,18 = 12.383, p = 0.002) and session (session: F6,108 = 10.799, p = 0.424), with no interactions by group (stimulus x group: F1,18 = 1.151, p = 0.298; session x group: F6,108 = 1.008, p = 0.424; stimulus x session: F6,108 = 5.440, p = 0.000; stimulus x session x group: F6,108 = 0.233, p = 0.965).

Extended Data Fig. 4 Responding during conditioning in the sensory-preconditioning experiment (see Fig. 5 main text).

Rates of responding are represented as time spent in the food port (%; ±SEM). Rats (n = 12 eYFP, n = 8 NpHR) learnt to distinguish between A2 and B2 during conditioning, with no difference in the rates or ultimate levels of learning between groups A repeated-measures ANOVA revealed a main effect of stimulus (F1,18 = 3.553, p = 0.076), and a stimulus x session interaction (F2,36 = 8.281, p = 0.001; stimulus × group: F1,18 = 0.13, p = 0.911), with follow-up comparisons, granted by the significant stimulus x session interaction, showing an increase in learning about A2 across sessions (F2,17 = 8.860, p = 0.002), that was not present with relation to B2 (F2,17 = 1.953, p = 0.172).

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Sharpe, M.J., Batchelor, H.M., Mueller, L.E. et al. Past experience shapes the neural circuits recruited for future learning. Nat Neurosci 24, 391–400 (2021). https://doi.org/10.1038/s41593-020-00791-4

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