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A quantitative reward prediction error signal in the ventral pallidum

Nature Neuroscience volume 23, pages 1267–1276 (2020)Cite this article

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Abstract

The nervous system is hypothesized to compute reward prediction errors (RPEs) to promote adaptive behavior. Correlates of RPEs have been observed in the midbrain dopamine system, but the extent to which RPE signals exist in other reward-processing regions is less well understood. In the present study, we quantified outcome history-based RPE signals in the ventral pallidum (VP), a basal ganglia region functionally linked to reward-seeking behavior. We trained rats to respond to reward-predicting cues, and we fit computational models to predict the firing rates of individual neurons at the time of reward delivery. We found that a subset of VP neurons encoded RPEs and did so more robustly than the nucleus accumbens, an input to the VP. VP RPEs predicted changes in task engagement, and optogenetic manipulation of the VP during reward delivery bidirectionally altered rats’ subsequent reward-seeking behavior. Our data suggest a pivotal role for the VP in computing teaching signals that influence adaptive reward seeking.

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Fig. 1: A subset of VP neurons signal preference-based RPEs.
Fig. 2: RPE encoding is more prevalent and robust in the VP than in the NAc.
Fig. 3: An expanded value space reveals stronger RPE signaling in the VP.
Fig. 4: VP reward activity tracks changes in trial-by-trial task engagement.
Fig. 5: Manipulation of VP reward activity bidirectionally alters task engagement.
Fig. 6: VP RPE neuron signaling adapts across reward blocks.

Data availability

The data generated and analyzed for this manuscript are available publicly at https://doi.org/10.12751/g-node.3lbd0c and ref. 51.

Code availability

The code used to analyze and visualize the data in this manuscript are available as Supplementary software and online at https://doi.org/10.12751/g-node.3lbd0c and ref. 51.

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Acknowledgements

This work was supported by the National Institutes of Health (grant nos. 5T32NS91018-17 (to D.J.O.), F30MH110084 (to B.A.B.), K99AA025384 (to J.M.R.), R01DA042038 and R01NS104834 (to J.Y.C.), and R01DA035943 (to P.H.J.)), by Klingenstein-Simons, MQ, NARSAD, and Whitehall (to J.Y.C.), by a NARSAD Young Investigator Award (to J.M.R.) and by the National Science Foundation Graduate Research Fellowship (grant no. DGE1746891 to D.J.O.). We thank K. Wang and X. Tong for technical assistance.

Author information

Author notes

  1. These authors contributed equally: David J. Ottenheimer, Bilal A. Bari.

Authors and Affiliations

  1. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA

    David J. Ottenheimer, Bilal A. Bari, Elissa Sutlief, Jeremiah Y. Cohen & Patricia H. Janak

  2. Brain Science Institute, Johns Hopkins University, Baltimore, MD, USA

    Bilal A. Bari & Jeremiah Y. Cohen

  3. Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA

    Kurt M. Fraser, Tabitha H. Kim, Jocelyn M. Richard & Patricia H. Janak

  4. Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA

    Jocelyn M. Richard

  5. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA

    Jeremiah Y. Cohen & Patricia H. Janak

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Contributions

D.J.O., J.M.R. and P.H.J. designed the experiments. D.J.O. collected the electrophysiology data. D.J.O., K.M.F. and T.H.K. collected the optogenetic data. B.A.B. designed and fit the models in consultation with D.J.O. D.J.O., B.A.B. and E.S. analyzed and visualized the data. D.J.O., B.A.B., J.M.R., J.Y.C. and P.H.J. interpreted the data. D.J.O., B.A.B. and P.H.J. prepared the manuscript with comments from E.S., K.M.F., T.H.K., J.M.R. and J.Y.C.

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Correspondence to Patricia H. Janak.

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Extended data

Extended Data Fig. 1 Placements for random sucrose/maltodextrin, random sucrose/maltodextrin/water, and blocked sucrose/maltodextrin rats.

Recording locations for nucleus accumbens (left) and ventral pallidum (right) rats.

Extended Data Fig. 2 Evaluation of model fitting.

(a) Distribution of the learning rate, α, for RPE neurons in VP (green) and NAc (orange). (b) Likelihood (LH) per trial for RPE and Current outcome neurons (n = 72 RPE and 126 Current outcome neurons from 5 rats) for RPE and Current outcome models, relative to the LH per trial of the Unmodulated model. Lower (more negative) indicates a better fit. Line represents median, box represents 25th and 75th percentile, and whiskers extend to 1.5 times the interquartile range. Red highlights the AIC-selected model. Median [25th to 75th percentile; min to max] ∆LH/trial are: RPE neurons, RPE model -0.21 [-0.39 to −0.14; −3.16 to −0.05], RPE neurons, Current outcome model −0.15 [−0.32 to −0.09; −3.03 to −0.02], Current outcome neurons, RPE model -0.12 [-0.23 to -0.07; -0.174 to -0.03], Current outcome neurons, Current outcome model -0.12 [-0.22 to -0.07; -1.73 to -0.03]. Median [25th-75th percentile] LH per trial for RPE neurons was 2.29 [2.04 to 2.49] and for Current outcome neurons was 2.15 [1.92 to 2.37]. (c) Model recovery, plotted as the fraction of neurons simulated with each model recovered as that model. (d) Distribution of difference between the true value of the parameters used to simulate the neurons in (c) and the values recovered by MLE.

Extended Data Fig. 3 Placements for optogenetic experiments.

(a) Expression of ArchT3.0:YFP and fiber tip placement for the rats included in the ArchT3.0 group for the optogenetic experiment in Fig. 3. (b) Expression of ChR2:GFP and fiber tip placement for the rats included in the ChR2 group. Pattern of results remained unchanged with or without inclusion of the rat with the most caudal placement.

Extended Data Fig. 4 Supplemental optogenetic data.

(a) Mean(+/−SEM) port occupancy in time surrounding reward delivery on laser and no laser trials for YFP (left, n = 7 rats) and ArchT (right, n = 7 rats) groups. (b) Mean(+/−SEM) port occupancy in time surrounding reward delivery on laser and no laser trials for GFP (left, n = 7 rats) and ChR2 (right, n = 11 rats) groups. To account for the disruption of port occupancy by laser stimulation, we ran our distance from port analysis on the time beyond 15 s past reward delivery and found the same pattern of results. (c) Additional optogenetic experiment in ChR2 rats and controls where the 2 sec of laser stimulation was at the onset of the cue. (d) Mean(+/−SEM) distance from port in the ITI following laser stimulation did not differ from no laser trials for GFP (p = 0.94, Wilcoxon signed-rank test, two-sided, n = 7 rats) or ChR2 (p = 0.11, Wilcoxon signed-rank test, two-sided, n = 10 rats) groups. (e) The effect of laser was similar across both groups (median: 0.06 GFP, n = 7 rats; -0.09 ChR2, n = 10 rats; p = 0.36, Wilcoxon rank-sum test, two-sided).

Extended Data Fig. 5 Value encoding in VP at the time of cue onset in the random sucrose/maltodextrin task.

(a) Schematic of model-fitting and neuron classification process. For each neuron, the reward outcome and spike count following reward delivery on each trial were used to fit two models: Value and Unmodulated. Akaike information criterion (AIC) was used to select the best model (right). (b) Mean(+/−SEM) activity of neurons best fit by each of the models, plotted according to previous outcome (n = 39 Value and 397 Unmodulated neurons from 5 rats). (c) Coefficients(+/−SE) for outcome history linear regression for each class of neurons (n = 39 Value and 397 Unmodulated neurons). (d) Mean(+/−SEM) activity of all Value neurons with trials binned by model-derived Value. (e) Mean(+/−SEM) population activity of simulated and actual Value neurons according to each trial’s Value (V). (f) Model recovery, plotted as the fraction of neurons simulated with each model recovered as that model.

Extended Data Fig. 6 Value encoding at the time of cue onset in the random sucrose/maltodextrin/water task.

(a) Fraction of VP neurons best fit by the Value and Unmodulated models in the random sucrose/maltodextrin/water task. (b) Mean(+/−SEM) activity of neurons best fit by each of the models, plotted according to previous outcome (n = 38 Value and 216 Unmodulated neurons from 3 rats). (c) Coefficients(+/−SE) for outcome history linear regression for each class of neurons (n = 38 Value and 216 Unmodulated neurons). (d) Mean(+/−SEM) population activity of simulated and actual Value neurons according to each trial’s Value (V). (e) Mean(+/−SEM) activity of all Value neurons with trials binned by model-derived Value. (f) Distribution of correlations between individual VP neurons’ firing rates at cue onset on each trial and the distance from the port during the previous ITI. * = p = 0.00001 for negative shift in mean correlation coefficient (vertical line) compared to 1000 shuffles of data for Value neurons, Wilcoxon signed-rank test, two-sided, as well as p = 0.0000002 for more negative coefficients for Value neurons compared to Unmodulated neurons, Wilcoxon rank-sum test, two-sided. See also Fig. 4c,d.

Extended Data Fig. 7 Placements for predictable and random sucrose/maltodextrin rats.

Recording locations for rats from predictable and random sucrose/maltodextrin experiment in Extended Data Fig. 8.

Extended Data Fig. 8 Impact of specific cue-derived predictions on VP firing.

(a) Task schematic: three auditory cues indicated three trial types. (b) Median latency to enter reward port following onset of cue for each trial type, plotted as the mean(+/−SEM) across all sessions for each rat (gray lines, n = 8, 9, 10, and 10 sessions for the 4 rats) and the overall mean(+/−SEM) (n = 37 sessions). (c) Percentage sucrose of total solution consumption in a two-bottle choice, before (‘Initial’) and after (‘Final’) recording (n = 4 rats). (d) Mean(+/−SEM) lick rate relative to reward delivery for each trial type (n = 37 sessions from 4 rats). (e) Mean(+/−SEM) activity of all neurons recorded in the predictable and random sucrose/maltodextrin task, aligned to reward delivery (n = 487 neurons from 4 rats). (f) Schematic of cue model-fitting. The best model (of 6 total) was selected with Akaike information criterion. (g) Fraction of the population best fit by each model. (h) Coefficients(+/−SE) for outcome history regression for each class of neurons with no cue effect (n = 38 RPE, 135 Current outcome, and 204 Unmodulated neurons). (i) Mean(+/−SEM) activity of all RPE neurons with no cue effect (n = 38 neurons). The trials for each neuron are binned according to their model-derived RPE. (j) Population activity of simulated and actual VP RPE neurons with no cue effect according to each trial’s RPE value. (k) Scatterplot of each cue effect neuron’s weight for specific sucrose and maltodextrin cues (n = 7 RPE, 33 Current outcome, and 70 Unmodulated cells with cue effects). The percentage of neurons falling in each quadrant is indicated. The percentage in our quadrant of interest (positive value for sucrose and negative value for maltodextrin) did not differ from chance (p = 0.1 for exact binomial test compared to null of 25%). (l) Mean(+/−SEM) activity of neurons with sucrose values > 0 and maltodextrin values < 0, consistent with a value-based cued expectation modulation. (m) Neurons with cue effects for cue-evoked signaling, rather than reward-evoked signaling, as in (g). (n) As in (k), for activity at the time of the cue rather than time of reward (n = 143 neurons with cue effects). * = p = 0.00001 for exact binomial test compared to null of 25%. (o) As in (l), for activity at the time of the cue rather than time of reward.

Extended Data Fig. 9 Classifying neurons with BIC instead of AIC.

(a) Fraction of neurons classified as RPE, Current outcome, and Unmodulated in VP and NAc in the random sucrose/maltodextrin task using Bayesian information criterion (BIC) as the selection criterion. (b) Coefficients(+/−SE) for outcome history regression for VP neurons of each BIC subset (n = 37 RPE, 110 Current outcome, and 289 Unmodulated cells from 5 rats). (c) Population mean(+/−SEM) of all VP BIC RPE neurons, binned according to the model-derived RPE. (d) Mean(+/−SEM) population activity of simulated and actual BIC RPE neurons according to each trial’s RPE value for VP (left) and NAc (right). (e) Distribution of correlations between model-predicted and actual spiking for all RPE neurons from each region. (f) Distribution of α for RPE neurons in VP (green) and NAc (orange). (g) Mean(+/−SEM) activity of VP neurons classified as RPE by AIC but not BIC according to current and previous outcome (n = 35 neurons). (h) Coefficients(+/−SE) for outcome history regression for these neurons. (i) Mean(+/−SEM) activity of these neurons binned according to model-derived RPE on each trial.

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Ottenheimer, D.J., Bari, B.A., Sutlief, E. et al. A quantitative reward prediction error signal in the ventral pallidum. Nat Neurosci 23, 1267–1276 (2020). https://doi.org/10.1038/s41593-020-0688-5

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