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.

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.