Neural activity encoding recent experiences is replayed during sleep and rest to promote consolidation of memories. However, precisely which features of experience influence replay prioritisation to optimise adaptive behaviour remains unclear. Here, we trained adult male rats on a novel maze-based reinforcement learning task designed to dissociate reward outcomes from reward-prediction errors. Four variations of a reinforcement learning model were fitted to the rats' behaviour over multiple days. Behaviour was best predicted by a model incorporating replay biased by reward-prediction error, compared to the same model with no replay, random replay or reward-biased replay. Neural population recordings from the hippocampus and ventral striatum of rats trained on the task evidenced preferential reactivation of reward-prediction and reward-prediction error signals during post-task rest. These insights disentangle the influences of salience on replay, suggesting that reinforcement learning is tuned by post-learning replay biased by reward-prediction error, not by reward per se. This work therefore provides a behavioural and theoretical toolkit with which to measure and interpret the neural mechanisms linking replay and reinforcement learning.

Axons of dopaminergic neurons express gamma-aminobutyric acid type-A receptors (GABARs) and nicotinic acetylcholine receptors (nAChRs), which are positioned to shape striatal dopamine release. We examine how interactions between GABARs and nAChRs influence dopaminergic axon excitability. Axonal patch-clamp recordings reveal that potentiation of GABARs by benzodiazepines suppress dopaminergic axon responses to cholinergic interneuron transmission. In imaging experiments, we use the first temporal derivative of axonal calcium signals to distinguish between direct stimulation of dopaminergic axons and nAChR-evoked activity. Inhibition of GABARs with gabazine selectively enhance nAChR-evoked axonal calcium signals but does not alter the strength or dynamics of acetylcholine release, suggesting that the enhancement is mediated primarily by GABARs on dopaminergic axons. Unexpectedly, we find that a widely used GABAR antagonist, picrotoxin, inhibits axonal nAChRs and should be used cautiously for striatal circuit analysis. Overall, we demonstrate that GABARs on dopaminergic axons regulate integration of nicotinic input to shape axonal excitability.

Intrinsic uncertainty in the reward environment requires the brain to run multiple models simultaneously to predict outcomes from preceding cues or actions. For example, reward outcomes may be linked to specific stimuli and actions, corresponding to stimulus- and action-based learning. But how does the brain arbitrate between such models? Here, we combined multiple computational approaches to quantify concurrent learning in male monkeys performing tasks with different levels of uncertainty about the model of the environment. By comparing behavior in control monkeys and monkeys with bilateral lesions to the amygdala or ventral striatum, we found evidence for a dynamic, competitive interaction between stimulus-based and action-based learning, and for a distinct role of the amygdala in model arbitration. We demonstrated that the amygdala adjusts the initial balance between the two learning systems and is essential for updating arbitration according to the correct model, which in turn alters the interaction between arbitration and learning that governs the time course of learning and choice behavior. In contrast, VS lesions lead to an overall reduction in stimulus-value signals. This role of the amygdala reconciles existing contradictory observations and provides testable predictions for future studies into circuit-level mechanisms of flexible learning and choice under uncertainty.

Basal Ganglia Advances is a collection highlighting research on the structure, function, and disorders of the basal ganglia. It features studies spanning neuroscience, clinical insights, and computational models, serving as a hub for advances in movement, cognition, and behavior.

Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

Dopamine (DA) is essential for the production of vigorous actions, but how DA modifies the gain of motor commands remains unclear. Here we show that subsecond DA transients in the striatum of mice are neither required nor sufficient for specifying the vigor of ongoing forelimb movements. Our findings have important implications for our understanding of how DA contributes to motor control under physiological conditions and in Parkinson's disease.

The substantia nigra pars reticulata (SNr), a key basal ganglia output nucleus, is modulated by dopamine (DA) believed to be released locally from midbrain DA neurons. Although DA has been proposed to regulate γ-aminobutyric acid (GABA) release from medium spiny neuron (MSN) terminals via presynaptic D1 receptors, the precise mechanisms remain unclear. Using presynaptic optical recordings of synaptic vesicle fusion, calcium influx in D1-MSN synapses together with postsynaptic patch-clamp recordings from SNr neurons, we found that DA inhibits D1-MSN GABA release in a frequency-dependent manner. Unexpectedly, this effect was independent of DA receptors and instead required 5-HT1B receptor activation. Using two-photon serotonin biosensor imaging in slices and fiber photometry in vivo, we demonstrate that DA enhances extracellular serotonin in the SNr via inhibition of serotonin reuptake. Our results suggest that serotonin mediates DAergic control of basal ganglia output and contributes to the therapeutic actions of dopaminergic medications for Parkinson's disease and psychostimulant-related disorders.

Many prosocial and antisocial behaviors simultaneously impact both ourselves and others, requiring us to learn from their joint outcomes to guide future choices. However, the neurocomputational processes supporting such social learning remain unclear. Across three pre-registered studies, participants learned how choices affected both themselves and others. Computational modeling tested whether people simulate how other people value their choices or integrate self- and other-relevant information to guide choices. An integrated value framework, rather than simulation, characterizes multi-outcome social learning. People update the expected value of choices using different types of prediction errors related to the target (e.g., self, other) and valence (e.g., positive, negative). This asymmetric value update is represented in brain regions that include ventral striatum, subgenual and pregenual anterior cingulate, insula, and amygdala. These results demonstrate that distinct encoding of self- and other-relevant information guides future social behaviors across mutually beneficial, mutually costly, altruistic, and instrumentally harmful scenarios.