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The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that in vivo activity of dopamine neurons deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive depolarizing shift in action potential threshold, and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error coding in dopamine neurons by tuning action potential threshold dynamics.
The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that in vivo activity of dopamine neurons deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive depolarizing shift in action potential threshold, and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error coding in dopamine neurons by tuning action potential threshold dynamics.
Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed in a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, i.e. the temporal difference error. A major difference between these areas was the overall activity level of reward responses: reward responses in dorsolateral striatum (DLS) were positively shifted, lacking inhibitory responses to negative prediction error. Tenets of habit and skill can be explained by this positively biased dopamine signal in DLS.
Inhibition of midbrain dopamine neurons is thought to underlie the signaling of events that are less rewarding than expected and drive learning based on these negative prediction errors. It has recently been shown that Kv4.3 channels influence the integration of inhibitory inputs in specific subpopulations of dopamine neurons. The functional properties of Kv4.3 channels are themselves strongly determined by the binding of auxiliary β -subunits; among them KChIP4a stands-out for its unique combination of modulatory effects. These include decreasing surface membrane trafficking and slowing inactivation kinetics. Therefore, we hypothesized that KChIP4a expression in dopamine neurons could play a crucial role in behavior, in particular by affecting the computation of negative prediction errors. We developed a mouse line where the alternative exon that codes for the KChIP4a splice variant was selectively deleted in midbrain dopamine neurons. In a reward-based reinforcement learning task, we observed that dopamine neuron-specific KChIP4a deletion selectively accelerated the rate of extinction learning, without impacting the acquisition of conditioned responses. We further found that this effect was due to a faster decrease in the initiation rate of goal-directed behaviors, and not faster increases in action disengagement. Furthermore, computational fitting of the behavioral data with a Rescorla-Wagner model confirmed that the observed phenotype was attributable to a selective increase in the learning rate from negative prediction errors. Finally, KChIP4a deletion did not affect performance in other dopamine-sensitive behavioral tasks that did not involve learning from disappointing events, including an absence of effects on working memory, locomotion and novelty preference. Taken together, our results demonstrate that an exon-and midbrain dopamine neuron-specific deletion of an A-type K + channel β -subunit leads to a selective gain of function in extinction learning. One Sentence Summary:Exon-and midbrain dopamine neuron-specific deletion of the Kv4 channel β -subunit KChIP4a selectively accelerates extinction learning
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