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The timing of action determines reward prediction signals in identified midbrain dopamine neurons


Animals adapt their behavior in response to informative sensory cues using multiple brain circuits. The activity of midbrain dopaminergic neurons is thought to convey a critical teaching signal: reward-prediction error. Although reward-prediction error signals are thought to be essential to learning, little is known about the dynamic changes in the activity of midbrain dopaminergic neurons as animals learn about novel sensory cues and appetitive rewards. Here we describe a large dataset of cell-attached recordings of identified dopaminergic neurons as naive mice learned a novel cue–reward association. During learning midbrain dopaminergic neuron activity results from the summation of sensory cue-related and movement initiation-related response components. These components are both a function of reward expectation yet they are dissociable. Learning produces an increasingly precise coordination of action initiation following sensory cues that results in apparent reward-prediction error correlates. Our data thus provide new insights into the circuit mechanisms that underlie a critical computation in a highly conserved learning circuit.

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Fig. 1: Juxtacellular recording from identified mDA neurons in awake, behaving mice.
Fig. 2: Peri-movement excitation of mDA neurons, but not inhibition, depends on reward context.
Fig. 3: Initial mDA reward responses encode reward-related movements.
Fig. 4: mDA neuron responses to predictive cue and reward stimuli evolve independently during acquisition learning.
Fig. 5: Peri-movement activity reflects reward expectation and sums with cue responses.
Fig. 6: Physiological mDA stimulation supports learning but is insufficient to provoke movement initiation.
Fig. 7: Time course of RPE correlates in mDA neurons is determined by the timing of action initiation.
Fig. 8: mDA neuron responses are consistent with temporal summation of sensory cue and action initiation components.

Data availability

The data used to generate the results that support the findings of this study are available from the corresponding authors upon reasonable request.


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We thank members of the J.T.D. laboratory, K. Bittner, C. Grienberger, D. Hunt, J. Macklin, J. Cohen, and R. Egnor for technical guidance; members of the J.T.D laboratory and members of the V. Jayaraman laboratory, B. Mensh, A. Lee, G. Rubin, and J. Day for project feedback; R. Rogers, J. Arnold, and C. Loper for assistance with behavioral rig design and implementation; and S. Lindo for assistance with surgeries. This work was supported by the Howard Hughes Medical Institute. J.T.D. is supported by Janelia.

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Data collection and analysis were performed by L.T.C. with input from J.T.D. Simulations were implemented by J.T.D. with input from L.T.C. All other aspects of the work were the product of both authors.

Corresponding authors

Correspondence to Luke T. Coddington or Joshua T. Dudman.

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Integrated supplementary information

Supplementary Figure 1 Properties and characterization of recordings.

a, Section from DAT-Cre::ai32 brain expressing eYFP under the control of the DAT promoter, additionally stained for tyrosine hydroxylase (TH). White scale bar, 1 mm. Representative of results from three independent experiments. b, Mean firing rates (top) and biphasic action potential duration (see Brown & Magill J. Neuro. 2009) for dopaminergic neurons recorded during (n = 96) and before (n = 29) animals experienced reward training. Bars indicate mean ± s.e.m. c, A small but significant difference existed between mDA neurons excited at movement initiation versus those inhibited or not modulated (one-way ANOVA, F = 10.5, P < 0.0001; Tukey’s post hoc, excited versus inhibited P < 0.0001, excited versus non-modulated P = 0.001, inhibited versus non-modulated P = 1). Bars indicate mean ± s.e.m. At right, mean ± s.e.m. spiking PETH aligned to movement initiation for neurons excited (red) versus inhibited (blue) around movement initiation. di, Left, comparing mean firing rates for mDA modulated (n = 75) versus not modulated (n = 21) at water delivery (t-test, p = 0.75, bars indicate mean ± s.e.m.), and (right) mean firing rate PETH and lick rate PETH during cued reward trials for the mDA neurons not modulated at water delivery (n = 21). The shaded area indicates s.e.m. dii, Left, mean ± s.e.m. firing rate for 21 non-reward modulated mDA neurons aligned to movement initiation. Right, mean effect of movement on firing rate, n =21; bars indicate mean ± s.e.m.

Supplementary Figure 2 ‘Covert’ excitation in the form of spike phase advances in the absence of significant modulation of mean firing rates.

a, Comparison of spike rasters either aligned to random spikes drawn from periods of stillness (top, gray) or aligned to the last spike before the movement-initiation-related pause (middle, green), with the cumulative density function at bottom, from the cell shown in a. b, Interspike intervals from the 1-s baseline prior to movement onset were significantly slower than for the last spikes before movement onset (two-tailed t test, n = 28, P = 0.009). c, The same analysis of spike phase advance for neurons recorded during reward training that lacked significant movement-aligned excitation (as determined by one-tailed sign-rank test P > 0.05). Two tailed t test, n = 73, P < 0.0001. *In b and c, colors reflect neurons recorded in the SNc (dark blue) or VTA (cyan).

Supplementary Figure 3 Putatively ‘silent’ solenoid is undetectable.

a, We tested for solenoid-related sounds at the point of mouse head fixation within the behavior rig by recording at 400 kHz with a Bruel & Kjaer 1/4" ultrasonic microphone (4939) with preamplifier (2670), amplified to 1 V/Pa with a Bruel and Kjaer Nexus microphone amplifier (2690-A). Data were filtered with a band pass of 1 to 200 kHz. b, Mean sound intensity across solenoid valve openings was calculated for the ultrasonic range and averaged across solenoid valve openings (indicated in a). The shaded area reflects s.e.m. c, Data summarized from n = 4 mice trained with an audible solenoid (>8 sessions) in which a ‘silent’ solenoid was triggered during ITI on ~ 20% of trials during a session. No modulation of behavior, either body movement (top) or licking (bottom), was apparent. The shaded area reflects s.e.m.

Supplementary Figure 4 Cue and reward activity simulation incorporating independently learned responses scaled by a common factor replicates observed relationships in the data.

Left, example results plotting the phasic modulation of activity (arbitrary units) as a function of trials for a simulation of the equations governing the change in dopamine neuron activity (ΔDA) at the time of the predictive tone (ΔDAtone) and the reward (ΔDAreward). The qquation is explicitly shown at right. Random numbers were drawn from a normal distribution (Nscaling). Right, Pearson’s correlation coefficients were calculated for all trials in the simulation (n = 1,000) for comparison with ‘observed’ Pearson’s correlations taken from the main text. Throughout the figure, red corresponds to the tone responses and black corresponds to reward responses.

Supplementary Figure 5 mDA neurons do not encode within-bout movement.

a, Mean firing rate (top), movement (middle), and lick rate (bottom) for 23 mDA cell recordings where cells were significantly excited at movement initiation (as determined by one-tailed sign-rank test P < 0.05). The shaded area reflects s.e.m. b, Same as in a, but for the point of maximum basket displacement within each movement bout excluding the first 500 ms surrounding movement initiation. c, No significant difference was observed between baseline rates and rates during the within-bout movements shown in b (two-tailed t test, n = 23, P = 0.6).

Supplementary Figure 6 Locations of mDA axon fiber photometry recordings and reward correlates.

a, Left, locations of recording fibers in ventral striatum in mice bilaterally injected with jRCaMP1a in the VTA as verified by histology. Right, example mean ventral striatal dopamine axon responses in the mouse indicated by the shaded fiber at left, aligned to cued reward delivery after three sessions of training. Mean ± s.e.m. from 56 water deliveries. b, Same as in a, but for mice injected with jRCaMP1a in the SNc and fibers implanted in the dorsal striatum. The trace at right represents the mean ± s.e.m. from 59 water deliveries.

Supplementary Figure 7 Omission signals are independent from the positive RPE computation.

a, Comparison of PETHs aligned to reward delivery in predicted trials (red) and to the moment of omitted reward delivery in trials with no reward delivery following the predictive tone (blue) are shown for middle (left) and late (right) training epochs. b, PETHs aligned to reward delivery for actual omission trials (blue) compared with the inferred, putative subtractive prediction effect (pred. PETH – unpred. PETH, purple). c, Inferred effect of prediction on mDA modulation by reward delivery (pred. – unpred.) plotted as a function of the mean modulation a movement initiation (F). The inset P value indicates the result of Pearson’s correlation (n = 10, r = –0.07, P = 0.8). d, Omission response versus movement response for 17 mDA neurons recorded when animals received omission trials in late training. Pearson’s correlation results in inset. The dotted line represents the best-fit trend. e, Effect of prediction (predicted – unpredicted reward responses) versus movement responses in 65 mDA neurons recorded when animals received unpredicted reward trials. Pearson’s correlation results in inset. The dotted line represents the best-fit trend.

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Coddington, L.T., Dudman, J.T. The timing of action determines reward prediction signals in identified midbrain dopamine neurons. Nat Neurosci 21, 1563–1573 (2018).

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