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Evaluation of ambiguous associations in the amygdala by learning the structure of the environment

Nature Neuroscience volume 19, pages 965–972 (2016)Cite this article

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Abstract

Recognizing predictive relationships is critical for survival, but an understanding of the underlying neural mechanisms remains elusive. In particular, it is unclear how the brain distinguishes predictive relationships from spurious ones when evidence about a relationship is ambiguous, or how it computes predictions given such uncertainty. To better understand this process, we introduced ambiguity into an associative learning task by presenting aversive outcomes both in the presence and in the absence of a predictive cue. Electrophysiological and optogenetic approaches revealed that amygdala neurons directly regulated and tracked the effects of ambiguity on learning. Contrary to established accounts of associative learning, however, interference from competing associations was not required to assess an ambiguous cue–outcome contingency. Instead, animals' behavior was explained by a normative account that evaluates different models of the environment's statistical structure. These findings suggest an alternative view of amygdala circuits in resolving ambiguity during aversive learning.

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Figure 1: Reduced CS–US contingency results in reduced CS memory, irrespective of trial order and with or without changes in context memory.
Figure 2: Conditioning to the context is not required for contingency degradation.
Figure 3: Activation of LA pyramidal cells during unsignaled USs is required to learn the degraded CS–US contingency.
Figure 4: Degraded CS–US contingency leads to reduced enhancement of CS processing in the LA.
Figure 5: Comparison of the structure learning model (SLM) and behavioral data.

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References

  1. Maren, S. & Quirk, G.J. Neuronal signalling of fear memory. Nat. Rev. Neurosci. 5, 844–852 (2004).

    CAS  PubMed  Google Scholar 

  2. LeDoux, J.E. Emotion circuits in the brain. Annu. Rev. Neurosci. 23, 155–184 (2000).

    CAS  PubMed  Google Scholar 

  3. Herry, C. & Johansen, J.P. Encoding of fear learning and memory in distributed neuronal circuits. Nat. Neurosci. 17, 1644–1654 (2014).

    CAS  PubMed  Google Scholar 

  4. Gründemann, J. & Lüthi, A. Ensemble coding in amygdala circuits for associative learning. Curr. Opin. Neurobiol. 35, 200–206 (2015).

    PubMed  Google Scholar 

  5. Rescorla, R.A. Probability of shock in the presence and absence of CS in fear conditioning. J. Comp. Physiol. Psychol. 66, 1–5 (1968).

    CAS  PubMed  Google Scholar 

  6. Rescorla, R.A. & Wagner, A.R. A theory of pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. in Classical Conditioning II Current Research and Theory (eds. Black, A.H. & Prokasy, W.F.) 64–99 (Appleton-Century-Crofts, New York, 1972).

  7. Van Hamme, L.J. & Wasserman, E.A. Cue competition in causality judgments: the role of nonpresentation of compound stimulus elements. Learn. Motiv. 25, 127–151 (1994).

    Google Scholar 

  8. Stout, S.C. & Miller, R.R. Sometimes-competing retrieval (SOCR): a formalization of the comparator hypothesis. Psychol. Rev. 114, 759–783 (2007).

    PubMed  Google Scholar 

  9. Pearce, J.M. & Bouton, M.E. Theories of associative learning in animals. Annu. Rev. Psychol. 52, 111–139 (2001).

    CAS  PubMed  Google Scholar 

  10. Kamin, L.J. Attention-like processes in classical conditioning. in Miami Symposium On The Production Of Behavior Aversive Stimulation (ed. Jones, M.R.) 9–33 (University of Miami Press, 1968).

  11. Bauer, E.P., LeDoux, J.E. & Nader, K. Fear conditioning and LTP in the lateral amygdala are sensitive to the same stimulus contingencies. Nat. Neurosci. 4, 687–688 (2001).

    CAS  PubMed  Google Scholar 

  12. Holyoak, K.J. & Cheng, P.W. Causal learning and inference as a rational process: the new synthesis. Annu. Rev. Psychol. 62, 135–163 (2011).

    PubMed  Google Scholar 

  13. Kim, J.J., DeCola, J.P., Landeira-Fernandez, J. & Fanselow, M.S. N-Methyl-D-aspartate receptor antagonist APV blocks acquisition but not expression of fear conditioning. Behav. Neurosci. 105, 126–133 (1991).

    CAS  PubMed  Google Scholar 

  14. McNish, K.A., Gewirtz, J.C. & Davis, M. Disruption of contextual freezing, but not contextual blocking of fear-potentiated startle, after lesions of the dorsal hippocampus. Behav. Neurosci. 114, 64–76 (2000).

    CAS  PubMed  Google Scholar 

  15. Durlach, P.J. Effect of signaling intertrial unconditioned stimuli in autoshaping. J. Exp. Psychol. Anim. Behav. Process. 9, 374–389 (1983).

    CAS  PubMed  Google Scholar 

  16. Gunther, L.M. & Miller, R.R. Prevention of the degraded-contingency effect by signalling training trials. Q. J. Exp. Psychol. B 53, 97–119 (2000).

    CAS  PubMed  Google Scholar 

  17. Balleine, B.W., Killcross, A.S. & Dickinson, A. The effect of lesions of the basolateral amygdala on instrumental conditioning. J. Neurosci. 23, 666–675 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Bermudez, M.A. & Schultz, W. Responses of amygdala neurons to positive reward-predicting stimuli depend on background reward (contingency) rather than stimulus-reward pairing (contiguity). J. Neurophysiol. 103, 1158–1170 (2010).

    PubMed  Google Scholar 

  19. Rogan, M.T., Stäubli, U.V. & LeDoux, J.E. Fear conditioning induces associative long-term potentiation in the amygdala. Nature 390, 604–607 (1997).

    CAS  PubMed  Google Scholar 

  20. Chow, B.Y. et al. High-performance genetically targetable optical neural silencing by light-driven proton pumps. Nature 463, 98–102 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Johansen, J.P. et al. Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation. Proc. Natl. Acad. Sci. USA 111, E5584–E5592 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Goosens, K.A., Hobin, J.A. & Maren, S. Auditory-evoked spike firing in the lateral amygdala and Pavlovian fear conditioning: mnemonic code or fear bias? Neuron 40, 1013–1022 (2003).

    CAS  PubMed  Google Scholar 

  23. Prévost, C., McNamee, D., Jessup, R.K., Bossaerts, P. & O'Doherty, J.P. Evidence for model-based computations in the human amygdala during Pavlovian conditioning. PLoS Comput. Biol. 9, e1002918 (2013).

    PubMed  PubMed Central  Google Scholar 

  24. Saez, A., Rigotti, M., Ostojic, S., Fusi, S. & Salzman, C.D. Abstract context representations in primate amygdala and prefrontal cortex. Neuron 87, 869–881 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Pearl, J. Probabilistic Reasoning in Intelligent Systems (Morgan Kauffmann, San Mateo, California, USA, 1988).

  26. Griffiths, T.L. & Tenenbaum, J.B. Structure and strength in causal induction. Cognit. Psychol. 51, 334–384 (2005).

    PubMed  Google Scholar 

  27. Hall, G. & Symonds, M. Overshadowing and latent inhibition of context aversion conditioning in the rat. Auton. Neurosci. 129, 42–49 (2006).

    PubMed  Google Scholar 

  28. Witnauer, J.E. & Miller, R.R. Degraded contingency revisited: posttraining extinction of a cover stimulus attenuates a target cue's behavioral control. J. Exp. Psychol. Anim. Behav. Process. 33, 440–450 (2007).

    PubMed  PubMed Central  Google Scholar 

  29. Matzel, L.D., Schachtman, T.R. & Miller, R.R. Recovery of an overshadowed association achieved by extinction of the overshadowing stimulus. Learn. Motiv. 16, 398–412 (1985).

    Google Scholar 

  30. Koller, D. & Friedman, N. Probabilistic Graphical Models: Principles and Techniques, Chs. XVII and XVIII, pp. 726, 784 (MIT Press, 2009).

  31. Quirk, G.J. et al. Erasing fear memories with extinction training. J. Neurosci. 30, 14993–14997 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Karlsson, M.P., Tervo, D.G.R. & Karpova, A.Y. Network resets in medial prefrontal cortex mark the onset of behavioral uncertainty. Science 338, 135–139 (2012).

    CAS  PubMed  Google Scholar 

  33. Behrens, T.E.J., Woolrich, M.W., Walton, M.E. & Rushworth, M.F.S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).

    CAS  PubMed  Google Scholar 

  34. Likhtik, E. & Paz, R. Amygdala-prefrontal interactions in (mal)adaptive learning. Trends Neurosci. 38, 158–166 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Yu, A.J. & Dayan, P. Uncertainty, neuromodulation, and attention. Neuron 46, 681–692 (2005).

    CAS  PubMed  Google Scholar 

  36. Rosas, J.M., Todd, T.P. & Bouton, M.E. Context change and associative learning. Wiley Interdiscip. Rev. Cogn. Sci. 4, 237–244 (2013).

    PubMed  PubMed Central  Google Scholar 

  37. Courville, A., Daw, N.D., Gordon, G. & Touretzky, D.S. Model uncertainty in classical conditioning. Adv. Neural Inf. Process. Syst. 16, 977–984 (2004).

    Google Scholar 

  38. Gershman, S.J., Blei, D.M. & Niv, Y. Context, learning, and extinction. Psychol. Rev. 117, 197–209 (2010).

    PubMed  Google Scholar 

  39. Soto, F.A., Gershman, S.J. & Niv, Y. Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. Psychol. Rev. 121, 526–558 (2014).

    PubMed  PubMed Central  Google Scholar 

  40. Uwano, T., Nishijo, H., Ono, T. & Tamura, R. Neuronal responsiveness to various sensory stimuli, and associative learning in the rat amygdala. Neuroscience 68, 339–361 (1995).

    CAS  PubMed  Google Scholar 

  41. Romanski, L.M., Clugnet, M.C., Bordi, F. & LeDoux, J.E. Somatosensory and auditory convergence in the lateral nucleus of the amygdala. Behav. Neurosci. 107, 444–450 (1993).

    CAS  PubMed  Google Scholar 

  42. Weinberger, N.M. Auditory associative memory and representational plasticity in the primary auditory cortex. Hear. Res. 229, 54–68 (2007).

    PubMed  PubMed Central  Google Scholar 

  43. Pecevski, D., Buesing, L. & Maass, W. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Comput. Biol. 7, e1002294 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Lochmann, T. & Deneve, S. Neural processing as causal inference. Curr. Opin. Neurobiol. 21, 774–781 (2011).

    CAS  PubMed  Google Scholar 

  45. Nessler, B., Pfeiffer, M., Buesing, L. & Maass, W. Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Comput. Biol. 9, e1003037 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Pouget, A., Beck, J.M., Ma, W.J. & Latham, P.E. Probabilistic brains: knowns and unknowns. Nat. Neurosci. 16, 1170–1178 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Chistiakova, M., Bannon, N.M., Bazhenov, M. & Volgushev, M. Heterosynaptic plasticity: multiple mechanisms and multiple roles. Neuroscientist 20, 483–498 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. Abraham, W.C. How long will long-term potentiation last? Phil. Trans. R. Soc. Lond. B 358, 735–744 (2003).

    Google Scholar 

  49. Friedman, N. & Koller, D. Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Mach. Learn. 50, 95–125 (2003).

    Google Scholar 

  50. Kappel, D., Habenschuss, S., Legenstein, R. & Maass, W. Network plasticity as Bayesian inference. PLoS Comput. Biol. 11, e1004485 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. Johansen, J.P., Tarpley, J.W., LeDoux, J.E. & Blair, H.T. Neural substrates for expectation-modulated fear learning in the amygdala and periaqueductal gray. Nat. Neurosci. 13, 979–986 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Gardner, W.J. Ma and C. Yokoyama for advice and comments on the manuscript and N. Daw for discussions during the course of this work. We thank the UNC vector core and E. Boyden (MIT) for the lentiviral vectors. This study was funded by National Institute of Mental Health (NIMH) grants R01-MH046516 and R01-MH38774, National Institute on Drug Abuse (NIDA) grant DA029053 (J.E.L.), MEXT Strategic Research Program for Brain Sciences (11041047) and Grants-in-Aid for Scientific Research (25710003, 25116531, 15H04264, 16H01291, 15H01301) (J.P.J.), and an NYU Williamson Fellowship (T.J.M.).

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Authors and Affiliations

  1. Center for Neural Science, New York University, New York, New York, USA

    Tamas J Madarasz, Lorenzo Diaz-Mataix, Omar Akhand & Joseph E LeDoux

  2. RIKEN Brain Science Institute, Wako, Saitama, Japan

    Tamas J Madarasz, Edgar A Ycu & Joshua P Johansen

  3. The Emotional Brain Institute, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA

    Joseph E LeDoux

  4. Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan

    Joshua P Johansen

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  1. Tamas J Madarasz
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Contributions

T.J.M. designed the experiments, collected and analyzed data, developed the computational models and wrote the manuscript. J.P.J. designed the experiments, collected data and wrote the manuscript. L.D.-M. collected and analyzed electrophysiology data. O.A. collected data. E.A.Y. collected and analyzed single-cell electrophysiology data. J.E.L. contributed to data interpretation and the final version of the manuscript.

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Correspondence to Tamas J Madarasz or Joshua P Johansen.

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Integrated supplementary information

Supplementary Figure 1 Correlations between tone and context freezing by animal in each of the four groups in experiment 1.

Every animal is represented by a blue circle. Correlation was measured by Spearman’s rank correlation coefficient (ρ).

Supplementary Figure 2 Comparisons of context memory between groups are stable over time and invariant to the salience of the conditioning context.

(a) Minute-by-minute analysis of freezing during the context test for the groups in experiment 1 (and Pairings Last, included for illustration but not in the statistical analysis). A repeated measures ANOVA showed no Time*Contingency*Spacing interaction (n = 16,17,18,20,21,F1,288 = 1.96, P = 0.17). A comparison restricted to the massed condition (between CTL II and Pairings First) also showed no Time*Contingency interaction (F 1, 144) = 0.74, P =0.40). (b) Comparison of CTL II and Pairings First groups with conditioning and context test performed in a more salient context (lit by a visible light and with citrus odor). Reduction in Tone memory matched previous result (ratio between CTLII and Pairings First 0.63 vs. 0.66 originally), whereas Context memory was similar between the groups, as before. Error bars indicate s.e.m.

Supplementary Figure 3 Defecation gives similar results to freezing for experiments described in Figure 2.

(a) Contingency degradation in the Intermixed condition with APV infusion in dorsal hippocampus (DH) prior to conditioning, as measured by defecation during tone test. (n = 9,9, Mann-Whitney U test, U = 17.5, P = 0.04). (b) Impaired contextual aversive memory, as measured by defecation, following APV infusion in DH prior to conditioning (n = 9,7, Mann-Whitney U test, U = 9.5, P = 0.018). Error bars indicate s.e.m.

Supplementary Figure 4 Effect of repeated USs depends on contingencies.

(a) Behavioral data (left) and model simulation (right) for conditioning with 15, and 21 CS-US pairings (n=9, 7). Adding further shocks paired with the same tone CS (21 pairings in total) did not reduce tone memory strength. (b) Behavioral data (left) and model simulation (right) for the cover stimulus effect. Signaling shocks with a second CS (in this case a flashing light), instead of giving unsignaled shocks attenuates contingency degradation (comparison between Pairings First and Cover Stimulus groups, n = 18, 12, unpaired sample t-test, t28 = 2.42, * P = 0.022) Error bars indicate s.e.m.

Supplementary Figure 5 Location of the optical fiber tips for optogenetic experiments.

Supplementary Figure 6 Location of the electrode tips for electrophysiological experiments.

Supplementary Figure 7 Effects of contingency on amygdala LFP potentiation.

(a) Example traces before, and after conditioning for a representative animal each in the Control II group (left) and Pairings First group (right). Red arrows indicate the peak depolarization. (b) Averaged peak depolarizations in the CTL II and Pairings First groups before (Habituation), and after (LTM) conditioning. There was a marginally significant interaction between time and contingency (n=10, 8, repeated measures ANOVA, F1,16 = 4.30, P=0.055), and a simple effects analysis showed significant potentiation of the LFP response in the CTL II, but not the Pairings First condition (F1,16 = 18.0, P = 0.001 and F1,16 = 1.022, P = 0.33), further indicating that conditioning differentially effects synaptic processing depending on contingency. Error bars indicate s.e.m.

Supplementary Figure 8 Comparison of SLM to behavioral results for experiments described in Figure 1.

Direct comparison of behavioral data (top panel) and SLM (bottom panel) for experiment 1. Error bars indicate s.e.m.

Supplementary Figure 9 The graph structures used for SLM, extended to include a second discrete variable.

Supplementary Figure 10 SLM’s predictions for further conditioning phenomena.

(a) Cover stimulus effect: Replacing unsignaled USs by USs signaled by a second discrete cue (e.g. a light) reverses the effects of contingency degradation. (b) Overshadowing: Conditioning to a single cue (Tone) is reduced if it is trained in compound with a second cue (Light). (c) Recovery from overshadowing: Unreinforced presentations of the overshadowing second cue (Light) restores the level of responding to the first cue. (d) Blocking: Initial conditioning to a Light reduces subsequent conditioning to the Tone when the Tone is conditioned in compound with the Light.

Supplementary Figure 11 Graphical illustration of the fits of some of the different models compared.

(a) Behavioral Data. (b) Bayesian model that learned both structure and parameters (SPLM). (c) Bayesian model that learned parameters using Graph 6 (from Fig. 5a) and the best Beta priors for edge parameters. (d) Van Hamme and Wasserman’s extension of the Rescorla-Wagner model.

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Supplementary Figures 1–11, Supplementary Tables 1–8 and Supplementary Modeling (PDF 2320 kb)

Supplementary Methods Checklist (PDF 435 kb)

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Madarasz, T., Diaz-Mataix, L., Akhand, O. et al. Evaluation of ambiguous associations in the amygdala by learning the structure of the environment. Nat Neurosci 19, 965–972 (2016). https://doi.org/10.1038/nn.4308

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