Primary sensory neurons convert external stimuli into electrical signals, yet how heterogeneous neurons encode distinct sensations remains unclear. In vivo dorsal root ganglia (DRG) imaging with genetically-encoded Ca indicators (GECIs) enables mapping of neuronal activity from over 1800 neurons per DRG in live mice, offering high spatial and populational resolution. However, GECIs' slow Ca response kinetics limit the temporal accuracy of neuronal electrical dynamics. Genetically-encoded voltage indicators (GEVIs) provide real-time voltage tracking but often lack the brightness and dynamic range required for in vivo use. Here, we used soma-targeted ASAP4.4-Kv, a bright and fast positively tuned GEVI, to dissect temporal dynamics of DRG neuron responses to mechanical, thermal, or chemical stimulation in live male and female mice. ASAP4.4-Kv revealed previously unrecognized cell-to-cell electrical synchronization and robust dynamic transformations in sensory coding following tissue injury. Combining GEVI and GECI imaging empowers spatiotemporal analysis of sensory signal processing and integration mechanisms in vivo.

The basal ganglia (BG) are a collection of subcortical nuclei involved in motor control, sensorimotor integration, and procedural learning. They play a key role in the acquisition and adaptation of movements, a process driven by dopamine-dependent plasticity at cortico-striatal projections, which serve as BG input. However, BG output is not necessary for executing many well-learned movements. This raises a fundamental question: How can plasticity at BG input contribute to the acquisition and adaptation of movements which execution does not require BG output? Existing models of BG function often neglect the feedback dynamics within cortico-BG-thalamo-cortical circuitry and do not capture the interaction between the cortex and BG in movement generation and adaptation. In this work, we address the above question in a theoretical model of the BG-thalamo-cortical multiregional network, incorporating anatomical, physiological, and behavioral evidence. We examine how its dynamics influence the execution and reward-based adaptation of reaching movements. We demonstrate how the BG-thalamo-cortical network can shape cortical motor output through the combination of three mechanisms: i) the diverse dynamics emerging from its closed-loop architecture, ii) attractor dynamics driven by recurrent cortical connections, and iii) reinforcement learning via dopamine-dependent cortico-striatal plasticity. Our study highlights the role of the cortico-BG-thalamo-cortical feedback in efficient visuomotor adaptation. It also suggests a mechanism for early-stage acquisition of reaching movements through motor babbling. More generally, our model explains how the BG-cortical network refines motor output through its intricate closed-loop dynamics and dopamine-dependent plasticity at cortico-striatal synapses.

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.

Head-mounted miniaturized two-photon microscopes enable cellular-resolution recording of neural activity deep in the mouse brain during unrestrained behavior. Two-photon microscopy, however, is traditionally limited in frame rate by the necessity of scanning the excitation beam over a large field-of-view (FOV). Here, we present two types of multiplexed miniaturized two-photon microscopes (M-MINI2Ps) that preserve spatial resolution while increasing frame rate by simultaneously imaging two FOVs and demixing them temporally or computationally. We demonstrate large-scale (500 × 500 μm FOV) multiplane calcium imaging in visual and prefrontal cortices of freely moving mice during spontaneous exploration, social behavior, and auditory stimulus. The increased speed of M-MINI2Ps also enables two-photon voltage imaging at 400 Hz over a 380 × 150 μm FOV in freely moving mice. With compact footprints and compatibility with the open-source MINI2P, M-MINI2Ps enable high-speed recording of rapid neural dynamics and large-volume population activity in freely moving mice, providing a powerful tool for systems neuroscience.

In order to understand the retinal network, it is essential to identify functional connectivity among retinal neurons. For this purpose, imaging neuronal activity through fluorescent indicator proteins has been a promising approach, offering simultaneous measurements of neuronal activities from different regions of the circuit. In this study, we used genetically encoded indicators─Bongwoori-R3 for voltage or GCaMP6f for calcium─to visualize membrane voltage or calcium dynamics, respectively, as spatial maps within individual retinal ganglion cells from retinal tissues of photoreceptor-degenerated mice. Retinal voltage imaging was able to show current-evoked somatic spiking as well as subthreshold voltage changes, while calcium imaging showed changes in calcium concentrations evoked by current pulses in retinal ganglion cells. These results indicate that the combination of fluorescent protein sensors and high-speed imaging methods permits the imaging of electrical activity with cellular precision and millisecond resolution. Hence, we expect our method will provide a potent experimental platform for the study of retinal signaling pathways, as well as the development of retinal stimulation strategies in visual prosthesis.