Dissociable dopamine dynamics for learning and motivation –

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The dopamine projection from ventral tegmental area (VTA) to nucleus accumbens (NAc) is critical for motivation to work for rewards and reward-driven learning. How dopamine supports both functions is unclear. Dopamine cell spiking can encode prediction errors, which are vital learning signals in computational theories of adaptive behaviour. By contrast, dopamine release ramps up as animals approach rewards, mirroring reward expectation. This mismatch might reflect differences in behavioural tasks, slower changes in dopamine cell spiking or spike-independent modulation of dopamine release. Here we compare spiking of identified VTA dopamine cells with NAc dopamine release in the same decision-making task. Cues that indicate an upcoming reward increased both spiking and release. However, NAc core dopamine release also covaried with dynamically evolving reward expectations, without corresponding changes in VTA dopamine cell spiking. Our results suggest a fundamental difference in how dopamine release is regulated to achieve distinct functions: broadcast burst signals promote learning, whereas local control drives motivation.

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Data availability

The AAV.Synapsin.dLight1.3b virus used in this study has been deposited with Addgene (no. 125560; All data will be available through the Collaborative Research in Computational Neuroscience data sharing website (

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Custom MATLAB code is available on request from J.D.B.

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We thank P. Dayan, H. Fields, L. Frank, C. Donaghue and T. Faust for their comments on an early version of the manuscript, and V. Hetrick, R. Hashim and T. Davidson for technical assistance and advice. This work was supported by the National Institute on Drug Abuse, the National Institute of Mental Health, the National Institute on Neurological Disorders and Stroke, the University of Michigan, Ann Arbor, and the University of California, San Francisco.

Reviewer information

Nature thanks Margaret Rice and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. These authors contributed equally: Ali Mohebi, Jeffrey R. Pettibone


  1. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    • Ali Mohebi
    • , Jeffrey R. Pettibone
    •  & Joshua D. Berke
  2. Department of Neuroscience, Brown University, Providence, RI, USA

    • Arif A. Hamid
  3. Department of Chemistry, University of Michigan, Ann Arbor, MI, USA

    • Jenny-Marie T. Wong
    •  & Robert T. Kennedy
  4. Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    • Leah T. Vinson
    •  & Joshua D. Berke
  5. Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA

    • Tommaso Patriarchi
    •  & Lin Tian
  6. Weill Institute for Neurosciences and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, USA

    • Joshua D. Berke


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A.M. performed and analysed the electrophysiology and photometry, and applied the computational model. J.R.P. performed and analysed the microdialysis with assistance from J.-M.T.W. and supervision by R.T.K. A.A.H. developed the behavioural task and initial photometry setup, and performed the voltammetry. L.T.V. performed retrograde tracing and analysis. T.P. and L.T. developed the dLight sensor and shared expertise. J.D.B. designed and supervised the study, and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to
Joshua D. Berke.

Extended data figures and tables

  1. Extended Data Fig. 1 Microdialysis subregions and the reward rate parameter.

    a, Top left, anatomical definitions of the subregions examined with microdialysis. Brain atlas outlines in this figure were reproduced with permission from Paxinos and Watson, 200551. Other panels map the correlation between dopamine release and reward rate at individual probe placements in coronal (mm from bregma, B) and sagittal (mm from midline) planes. Colour bar shows strength of correlation. b, Top left, Regression analysis showing dependency of (log) latency on the outcome of recent trials, during microdialysis sessions (n = 26 sessions, 7,113 trials, from 12 rats; error bars show s.e.m.). *average regression weights significantly different from zero (t-test, P < 0.05). Top right, illustration of how the reward rate definition depends on the time constant (tau) of the leaky integrator. Top middle, dopamine: reward rate correlations as a function of τ. In the main Figs., τ was chosen (from a range of 1–1,200 s) to maximize the (negative) correlation between reward rate and (log) latency in each session. Thin lines represent individual sessions, with the best fit τ used in regression analyses indicated by a dot. Thick lines indicate the average of all dopamine: reward rate correlations for a given tau within each subregion. Overall behavioural metrics were similar between sessions sampling from each of the seven subregions (mean rewards per min: range 1.42–1.77, ANOVA F(6,44) = 0.58, P = 0.746; mean attempts per min: range 3.32–3.97, F(6,44) = 0.40, P = 0.872; mean latency: range 5.99–8.02, F(6,44) = 0.27, P = 0.948).

  2. Extended Data Fig. 2 Correlations between all neurochemicals and a range of behavioural factors.

    Bars represent R2 values for linear tests between each analyte (rows) and behavioural covariates (columns). In models with more than one covariate, bar length indicates the R2 for the full model. Negative relationships are reported in blue and positive relationships are in red. P values are reported at three alpha levels (0.05, 0.0005 and 0.000005) after Bonferroni correction for multiple comparisons (7 subregions × 21 analytes × 12 measures). To calculate reward rate, we averaged the leaky-integrator-estimated reward rate in 1-min bins defined by the start and end of each dialysis sample. ‘Attempts’ is the number of initiated trials (including trials that resulted in an error) in each dialysis minute. Attempts and reward rate and an interaction term were combined in a single model (column 2) to examine whether adding attempts could explain additional variance in the analyte signal that could not be explained by reward rate alone. ‘Latency’ is the average of the (log) latency in each minute. ‘Exploit’ is the proportion of choices of the higher reward probability option, in the last half of blocks for which the two ports had different probabilities. ‘Rewards’ and ‘omissions’ were defined as the number of rewarded and unrewarded trials in each minute, respectively. ‘Cumulative rewards’ and ‘time’ were included in the same regression model to estimate progressive factors such as satiety, and possible slow timescale increases or decreases in analyte concentration across the session. Cumulative rewards represents the total number of rewards received by the end of the current dialysis minute, and time was simply the number of minutes elapsed since the session began. Bars in this column show colour when only the coefficient for the cumulative reward variable was significant. %Ipsi and %Contra represent the fraction of choices to ipsi- or contra-versive ports (relative to probe location in the brain) in each minute, independent of block probability. P(win-stay) is the probability of repeating the previous choice, given the previous choice was rewarded.

  3. Extended Data Fig. 3 Histological analysis of electrophysiological recording locations.

    Left, atlas locations and histology photomicrographs for each rat (IM-657, IM-1002, IM-1003, IM-1037 and IM-1078) from which opto-tagged dopamine cells were obtained. Red, TH-staining; green: ChR2–eYFP; blue: DAPI. Scale bars, 1 mm. IM-1037 and IM-1078 brains were sliced horizontally, so fibre tracks appear as a circle. Font colours for rat ID numbers correspond to colours of tick marks in coronal atlas sections, indicating estimated recording locations for opto-tagged dopamine cells. For IM-1078, virus was injected into NAc core, and retrogradely infected dopamine neurons were recorded in VTA. Right, retrograde tracing of CTb from NAc core (top) to VTA-l (bottom). Top panel shows approximate extent of NAc labelling in each of the three rats (each rat indicated by a different colour). Bottom left panels show close-ups of TH labelling (blue), CTb (green) and merged image. Bottom right panels show reconstructed locations of TH+ and double-labelled TH+CTb+ midbrain neurons, on horizontal atlas sections. Estimated optrode locations are shown by red circles (or orange circle, in the case of the retrograde tagging rat IM-1078). Labelled neurons were counted within the red rectangles that span the AP and ML extent of estimated recording locations. Percentages shown are the fraction of TH+ neurons that are also CTb+. Brain atlas outlines in this figure were reproduced with permission from Paxinos and Watson, 200551.

  4. Extended Data Fig. 4 Identification of light-responsive cells.

    a, Average waveforms of optogenetically identified dopamine neurons (negative voltage upwards). Average light-evoked waveforms are shown in blue and session-wide average waveforms are in black. All spikes within 10 ms of laser onset were used to construct light-evoked waveform average. Averaged waveforms are normalized to have similar total peak-valley voltages (see Supplementary Fig. 1 for individual voltage ranges). b, Session-wide average waveform for non-dopamine cells. c, Opto-tagging P value for all units plotted in log-scale, showing a strong bimodal distribution. To classify cells as light-responsive we used a threshold of P < 0.001. d, Times to first spike after laser onset, showing mean for each identified dopamine neuron, and standard deviation (jitter).

  5. Extended Data Fig. 5 Dopaminergic responses to Pavlovian cues.

    a, Tone pips were followed by reward delivery (‘click’) with different probabilities (zero, medium or high) depending on the tone pitch. During prior training (average 15.6 sessions, range 2–26) rats had learned about these different probabilities, as indicated by their corresponding scaled likelihood of entering the food port during cue presentation. ‘Head entry %’ indicates proportion of trials for which the rat was at the food port at each moment in time, for one example session. Red and blue indicate rewarded and unrewarded trials, respectively. This rat was more likely to go to the food port during the cue that was highly (75%) predictive of rewards compared to the other cues (25% and 0%; one-way ANOVA, F = 11.1, P < 1.2 × 10−6). Unpredictable reward delivery (right) prompts rapid approach. Bottom, raster plots and peri-event time histograms from an identified dopamine neuron during that same session. b, Averaged firing for identified dopamine cells (n = 27) in this task. High/medium tones were either 75%/25% predictive of reward (n = 9 cells) or 100%/50% (n = 18), respectively. Data on each individual dopamine neuron are presented in the Supplementary Fig. 1. c, Behaviour (top), cue response (middle) and click response (bottom) for all Pavlovian sessions with opto-tagged dopamine cells. Statistical comparisons were all one-way ANOVA, using food port head entry during 0.3–3-s epoch relative to cue onset, and peak firing rate during 0.5-s duration epochs after cue onset or food-hopper clicks. d–f, Same as above except for dLight measurements (n = 10 sessions total). All dLight sessions used tones with 75, 25 and 0% reward probability, and ANOVA tests examined peak signal within 1 s of cue onset or food-hopper clicks.

  6. Extended Data Fig. 6 Results from each dLight recording session.

    Each row shows a distinct optic fibre placement, and the corresponding recording session that was included in data analyses. For two rats (IM-1066 and IM-1088) we obtained bilateral NAc dLight recordings. From left to right, panels show histologically determined NAc location of fibre tip (within horizontal brain atlas section, including atlas coordinates51), long timescale cross-correlation with reward rate (as in Fig. 3c), short timescale cross-correlation with reward rate (black), SMDP state value (green) and RPE (magenta; as in Fig. 3f); event-aligned averages (as in Fig. 4b, but including more events). For Light-on and Centre-in alignments data are split by latencies <1 s (light green) or >2 s (dark green; as in Fig. 4d); for other alignments, data are split by rewarded (red) and unrewarded (blue) trials. Brain atlas outlines in this figure were reproduced with permission from Paxinos and Watson, 200551.

  7. Extended Data Fig. 7 Comparing event-aligned activity between different signals.

    Format is as in Fig. 4. dLight fluorescence is here shown separately for 470-nm and 405-nm (control) excitation. Of note, (1) rapid, behaviour-linked dLight fluorescence changes occur at 470 nm, as expected, not in the control 405-nm band; (2) distinct timing of spiking, dLight, and voltammetry responses to cue onsets; and (3) non-dopamine cell firing is much more variable (wider error bands) but on average shows activity during movements: starting just before Centre-in (irrespective of latency), just before Side-in, and just before Food-port-in.

  8. Extended Data Fig. 8 Different methods for calculating reward expectation produce similar results.

    Left column, average firing rate of dopamine cells around Side-in, broken down by terciles of reward expectation, based either on recent reward rate (top; same as Fig. 5a), number of rewards in previous ten trials, state value (V) of an actor-critic model or state value (Qleft + Qright) of a Q-learning model. The actor-critic and Q-learning models were both trial-based, rather than evolving continuously in time. The actor-critic model estimated the overall probability of receiving a reward on each trial, V, using the update rule V′ = V + alpha(RPE), in which RPE = actual reward [1 or 0] − V. The Q-learning model kept separate estimates of the probabilities of receiving rewards for left and right choices (Qleft and Qright) and updated Q for the chosen action (only) using Q′ = Q + alpha(RPE), in which RPE = actual reward [1 or 0] – Q. The learning parameter alpha was determined for each session by best fit to latencies, for V or (Qleft + Qright) respectively. The subsequent columns show correlations between reward expectation and dopamine cell firing after Side-in, measuring either peak firing rate (within 250 ms after rewarded Side-in), minimum firing rate (middle; within 2 s after unrewarded Side-in) and pause duration (bottom; maximum inter-spike-interval within 2 s after unrewarded Side-in). For all histograms, light blue indicates cells with significant correlations (P < 0.01) before multiple comparisons correction, dark blue indicates cells that remained significant after correction. Positive RPE coding is strong and consistent, negative RPE coding is less so.

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25 May 2018


29 April 2019


22 May 2019



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