Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Past experience shapes the neural circuits recruited for future learning

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

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/.

References

  1. 1.

    Axelrod, R. Schema theory: an information processing model of perception and cognition. Am. Polit. Sci. Rev. 67, 1248–1266 (1973).

    Google Scholar 

  2. 2.

    Bem, S. L. Gender schema theory and its implications for child development: raising gender-aschematic children in a gender-schematic society. Signs J. Women Cult. Soc. 8, 598–616 (1983).

    Google Scholar 

  3. 3.

    Rumelhart, D. E. Schemata: the building blocks of cognition. in Theoretical Issues in Reading Comprehension: Perspectives from Cognitive Psychology, Linguistics, Artificial Intelligence and Education. 2nd edn (eds Bruce, B. C., Spiro, R. J. & Brewer, W. F.) 33–58 (Routledge, 2017).

  4. 4.

    Rau, V., DeCola, J. P. & Fanselow, M. S. Stress-induced enhancement of fear learning: an animal model of post-traumatic stress disorder. Neurosci. Biobehav. Rev. 29, 1207–1223 (2005).

    PubMed  Google Scholar 

  5. 5.

    Rau, V. & Fanselow, M. S. Exposure to a stressor produces a long lasting enhancement of fear learning in rats. Stress 12, 125–133 (2009).

    PubMed  Google Scholar 

  6. 6.

    Sillivan, S. E. et al. Susceptibility and resilience to post-traumatic stress disorder–like behaviors in inbred mice. Biol. Psychiatry 82, 924–933 (2017).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Holmes, N. M., Parkes, S. L., Killcross, A. S. & Westbrook, R. F. The basolateral amygdala is critical for learning about neutral stimuli in the presence of danger, and the perirhinal cortex is critical in the absence of danger. J. Neurosci. 33, 13112–13125 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Ponomarev, I., Rau, V., Eger, E. I., Harris, R. A. & Fanselow, M. S. Amygdala transcriptome and cellular mechanisms underlying stress-enhanced fear learning in a rat model of post-traumatic stress disorder. Neuropsychopharmacology 35, 1402–1411 (2010).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Nieh, E. H. et al. Inhibitory input from the lateral hypothalamus to the ventral tegmental area disinhibits dopamine neurons and promotes behavioral activation. Neuron 90, 1286–1298 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Sharpe, M. J. et al. Lateral hypothalamic GABAergic neurons encode reward predictions that are relayed to the ventral tegmental area to regulate learning. Curr. Biol. 27, 2089–2100 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Stuber, G. D. & Wise, R. A. Lateral hypothalamic circuits for feeding and reward. Nat. Neurosci. 19, 198–205 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Urstadt, K. R. & Berridge, K. C. Optogenetic mapping of feeding and self-stimulation within the lateral hypothalamus of the rat. PLoS ONE 15, e0224301 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Margules, D. & Olds, J. Identical ‘feeding’ and ‘rewarding’ systems in the lateral hypothalamus of rats. Science 135, 374–375 (1962).

    CAS  PubMed  Google Scholar 

  14. 14.

    Sutton, R. S. & Barto, A. G. in Proceedings of the Ninth Annual Conference of the Cognitive Science Society. 355–378 (Seattle, WA).

  15. 15.

    Seijen, H. & Sutton, R. in Proceedings of the 31st International Conference on Machine Learning. 692–700 (2014).

  16. 16.

    Sharpe, M. J. & Killcross, S. The prelimbic cortex directs attention toward predictive cues during fear learning. Learn. Mem. 22, 289–293 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Sharpe, M. J. & Killcross, S. Modulation of attention and action in the medial prefrontal cortex of rats. Psychol. Rev. 125, 822–843 (2018).

    PubMed  Google Scholar 

  18. 18.

    Mackintosh, N. J. A theory of attention: variations in the associability of stimuli with reinforcement. Psychol. Rev. 82, 276–298 (1975).

    Google Scholar 

  19. 19.

    Gardner, M. P. et al. Medial orbitofrontal inactivation does not affect economic choice. Elife 7, e38963 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Chang, C. Y., Gardner, M. P., Conroy, J. C., Whitaker, L. R. & Schoenbaum, G. Brief, but not prolonged, pauses in the firing of midbrain dopamine neurons are sufficient to produce a conditioned inhibitor. J. Neurosci. 38, 8822–8830 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Sharpe, M. J. et al. Dopamine transients do not act as model-free prediction errors during associative learning. Nat. Commun. 11, 1–10 (2020).

    Google Scholar 

  22. 22.

    Sharpe, M. J., Batchelor, H. M. & Schoenbaum, G. Preconditioned cues have no value. Elife 6, e28362 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Sharpe, M. J. et al. Dopamine transients are sufficient and necessary for acquisition of model-based associations. Nat. Neurosci. 20, 735 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Langdon, A. J., Sharpe, M. J., Schoenbaum, G. & Niv, Y. Model-based predictions for dopamine. Curr. Opin. Neurobiol. 49, 1–7 (2018).

    CAS  PubMed  Google Scholar 

  25. 25.

    Holland, P. C. CS–US interval as a determinant of the form of Pavlovian appetitive conditioned responses. J. Exp. Psychol. Anim. Behav. Process. 6, 155–174 (1980).

    CAS  PubMed  Google Scholar 

  26. 26.

    Lavin, M. J. The establishment of flavor–flavor associations using a sensory preconditioning training procedure. Learn. Motiv. 7, 173–183 (1976).

    Google Scholar 

  27. 27.

    Killcross, A., Dickinson, A. & Robbins, T. Effects of the neuroleptic α-flupenthixol on latent inhibition in aversively-and appetitively-motivated paradigms: evidence for dopamine-reinforcer interactions. Psychopharmacology 115, 196–205 (1994).

    CAS  PubMed  Google Scholar 

  28. 28.

    Lubow, R. & Moore, A. Latent inhibition: the effect of nonreinforced pre-exposure to the conditional stimulus. J. Comp. Physiol. Psychol. 52, 415–419 (1959).

    CAS  PubMed  Google Scholar 

  29. 29.

    Lubow, R. E. & Gewirtz, J. C. Latent inhibition in humans: data, theory and implications for schizophrenia. Psychol. Bull. 117, 87–103 (1995).

    CAS  PubMed  Google Scholar 

  30. 30.

    Jennings, J. H., Rizzi, G., Stamatakis, A. M., Ung, R. L. & Stuber, G. D. The inhibitory circuit architecture of the lateral hypothalamus orchestrates feeding. Science 341, 1517–1521 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Fanselow, M. S. & LeDoux, J. E. Why we think plasticity underlying Pavlovian fear conditioning occurs in the basolateral amygdala. Neuron 23, 229–232 (1999).

    CAS  PubMed  Google Scholar 

  32. 32.

    Maren, S. Neurotoxic basolateral amygdala lesions impair learning and memory but not the performance of conditional fear in rats. J. Neurosci. 19, 8696–8703 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ressler, R. L. & Maren, S. Synaptic encoding of fear memories in the amygdala. Curr. Opin. Neurobiol. 54, 54–59 (2019).

    CAS  PubMed  Google Scholar 

  34. 34.

    Holland, P. C. & Rescorla, R. A. Second-order conditioning with food unconditioned stimulus. J. Comp. Physiol. Psychol. 88, 459–467 (1975).

    CAS  PubMed  Google Scholar 

  35. 35.

    Rizley, R. C. & Rescorla, R. A. Associations in second-order conditioning and sensory preconditioning. J. Comp. Physiol. Psychol. 81, 1–11 (1972).

    CAS  PubMed  Google Scholar 

  36. 36.

    Brogden, W. J. Sensory pre-conditioning. J. Exp. Psychol. 25, 323–332 (1939).

    Google Scholar 

  37. 37.

    Ersche, K. D. et al. Carrots and sticks fail to change behavior in cocaine addiction. Science 352, 1468–1471 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Everitt, B. J. & Robbins, T. W. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat. Neurosci. 8, 1481–1489 (2005).

    CAS  PubMed  Google Scholar 

  39. 39.

    Corlett, P. R. & Fletcher, P. C. Delusions and prediction error: clarifying the roles of behavioural and brain responses. Cogn. Neuropsychiatry 20, 95–105 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Powers, A. R., Mathys, C. & Corlett, P. Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science 357, 596–600 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Mahn, M., Prigge, M., Ron, S., Levy, R. & Yizhar, O. Biophysical constraints of optogenetic inhibition at presynaptic terminals. Nat. Neurosci. 19, 554–556 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Howell, D. C. Statistical Methods for Psychology. (Cengage Learning, 2012).

  43. 43.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Loftus, G. R. & Masson, M. E. Using confidence intervals in within-subject designs. Psychon. Bull. Rev. 1, 476–490 (1994).

    CAS  PubMed  Google Scholar 

  45. 45.

    Franz, V. H. & Loftus, G. R. Standard errors and confidence intervals in within-subjects designs: Generalizing Loftus and Masson (1994) and avoiding the biases of alternative accounts. Psychon. Bull. Rev. 19, 395–404 (2012).

    PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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).

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing