Flexible control of motor timing is crucial for behaviour. Before volitional movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity in the cortico-basal ganglia loop may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviours, distinguishing their individual roles in this timer function remains challenging. Here, to address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a flexible lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. These findings are consistent with a model in which the striatum is part of a network that temporally integrates input from the frontal cortex and generates ramping activity that regulates motor timing.

All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translate into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we present the results of a computational approach to address this problem. Specifically, we simulated decisions during the early learning process driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit an evidence accumulation model to the resulting behavior. Using canonical correlation analysis, we then replicated the identification of three control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each of these subnetworks mapping to a specific configuration of the evidence accumulation process. We subsequently simulated learning in a simple two-choice task with one optimal (i.e., rewarded) target and found that, during early stages of learning, feedback-driven dopaminergic plasticity on cortico-striatal synapses effectively increases reward rate over time. The learning-related changes in the decision policy can be decomposed in terms of the contributions of each control ensemble, whose influence is driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts subnetworks within CBGT circuits so as to increase reward rate by strategically modulating how evidence is used to drive decisions.

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.

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.