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Congratulations are in order to Associate Professor Ana Domingos on her appointment to the Advisory Board of the top research journal dedicated to publishing novel, impactful papers spanning basic to clinical metabolic research.
Dopamine Axons in Dorsal Striatum Encode Contralateral Visual Stimuli and Choices.
The striatum plays critical roles in visually-guided decision-making and receives dense axonal projections from midbrain dopamine neurons. However, the roles of striatal dopamine in visual decision-making are poorly understood. We trained male and female mice to perform a visual decision task with asymmetric reward payoff, and we recorded the activity of dopamine axons innervating striatum. Dopamine axons in the dorsomedial striatum (DMS) responded to contralateral visual stimuli and contralateral rewarded actions. Neural responses to contralateral stimuli could not be explained by orienting behavior such as eye movements. Moreover, these contralateral stimulus responses persisted in sessions where the animals were instructed to not move to obtain reward, further indicating that these signals are stimulus-related. Lastly, we show that DMS dopamine signals were qualitatively different from dopamine signals in the ventral striatum (VS), which responded to both ipsilateral and contralateral stimuli, conforming to canonical prediction error signaling under sensory uncertainty. Thus, during visual decisions, DMS dopamine encodes visual stimuli and rewarded actions in a lateralized fashion, and could facilitate associations between specific visual stimuli and actions.SIGNIFICANCE STATEMENT While the striatum is central to goal-directed behavior, the precise roles of its rich dopaminergic innervation in perceptual decision-making are poorly understood. We found that in a visual decision task, dopamine axons in the dorsomedial striatum (DMS) signaled stimuli presented contralaterally to the recorded hemisphere, as well as the onset of rewarded actions. Stimulus-evoked signals persisted in a no-movement task variant. We distinguish the patterns of these signals from those in the ventral striatum (VS). Our results contribute to the characterization of region-specific dopaminergic signaling in the striatum and highlight a role in stimulus-action association learning.
Midbrain dopamine neurons signal phasic and ramping reward prediction error during goal-directed navigation.
Goal-directed navigation requires learning to accurately estimate location and select optimal actions in each location. Midbrain dopamine neurons are involved in reward value learning and have been linked to reward location learning. They are therefore ideally placed to provide teaching signals for goal-directed navigation. By imaging dopamine neural activity as mice learned to actively navigate a closed-loop virtual reality corridor to obtain reward, we observe phasic and pre-reward ramping dopamine activity, which are modulated by learning stage and task engagement. A Q-learning model incorporating position inference recapitulates our results, displaying prediction errors resembling phasic and ramping dopamine neural activity. The model predicts that ramping is followed by improved task performance, which we confirm in our experimental data, indicating that the dopamine ramp may have a teaching effect. Our results suggest that midbrain dopamine neurons encode phasic and ramping reward prediction error signals to improve goal-directed navigation.
The Spineless Origins of Prefrontal Cortex Dysfunction and Psychiatric Disorders.
Fundamental research into early circuits of the neocortex provides insight into the etiology of mental illness. In this issue of Neuron, Chini et al. (2020) probe the consequences of combined genetic and environmental perturbation on emergent network activity in the prefrontal cortex, identifying a window for possible intervention.
Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon.
Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines the correct choice. Yet, numerous studies report that outcomes can bias perceptual decisions, causing spurious changes in choice behavior without improving accuracy. Here we show that the effects of reward on perceptual decisions are principled: past rewards bias future choices specifically when previous choice was difficult and hence decision confidence was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats, and humans, and sensory modalities from olfaction and audition to vision. We show that this choice-updating strategy can be explained by reinforcement learning models incorporating statistical decision confidence into their teaching signals. Thus, reinforcement learning mechanisms are continually engaged to produce systematic adjustments of choices even in well-learned perceptual decisions in order to optimize behavior in an uncertain world.
Dopamine encoding of novelty facilitates efficient uncertainty-driven exploration.
When facing an unfamiliar environment, animals need to explore to gain new knowledge about which actions provide reward, but also put the newly acquired knowledge to use as quickly as possible. Optimal reinforcement learning strategies should therefore assess the uncertainties of these action-reward associations and utilise them to inform decision making. We propose a novel model whereby direct and indirect striatal pathways act together to estimate both the mean and variance of reward distributions, and mesolimbic dopaminergic neurons provide transient novelty signals, facilitating effective uncertainty-driven exploration. We utilised electrophysiological recording data to verify our model of the basal ganglia, and we fitted exploration strategies derived from the neural model to data from behavioural experiments. We also compared the performance of directed exploration strategies inspired by our basal ganglia model with other exploration algorithms including classic variants of upper confidence bound (UCB) strategy in simulation. The exploration strategies inspired by the basal ganglia model can achieve overall superior performance in simulation, and we found qualitatively similar results in fitting model to behavioural data compared with the fitting of more idealised normative models with less implementation level detail. Overall, our results suggest that transient dopamine levels in the basal ganglia that encode novelty could contribute to an uncertainty representation which efficiently drives exploration in reinforcement learning.
Dopaminergic computations for perceptual decisions.
Studies linking the brain's dopamine signals with learning and decision making have enjoyed enormous progress using predominantly value-based decision-making tasks. However, recent studies have demonstrated pervasive dopamine signaling also during perceptual decision making. These signals have been shown to depend on both feedback and perceptual parameters such as perceptual decision confidence and sensory statistics. Here, we review recent studies investigating dopamine signals in simple and complex forms of perceptual decision tasks across species and dopaminergic circuits. We discuss how reinforcement learning (RL) models can account for key aspects of learning during perceptual decision making and its dopaminergic underpinnings, thus bridging the gap with the literature on dopamine in value-based decisions. Finally, we propose that RL may provide a promising framework to address current challenges in the dopamine literature, such as explaining the function of its heterogeneous responses and its role in learning from naive to expert.
An axonal brake on striatal dopamine output by cholinergic interneurons.
Depolarization of axons is necessary for somatic action potentials to trigger axonal neurotransmitter release. Here we show that striatal cholinergic interneurons (ChIs) and nicotinic receptors (nAChRs) on mouse dopamine axons interrupt this relationship. After nAChR-mediated depolarization, dopamine release by subsequent depolarization events was suppressed for ~100 ms. This suppression was not due to depletion of dopamine or acetylcholine, but to a limited reactivation of dopamine axons after nAChR-mediated depolarization, and is more prominent in dorsal than in ventral striatum. In vivo, nAChRs predominantly depressed dopamine release, as nAChR antagonism in dorsal striatum elevated dopamine detected with optic-fiber photometry of dopamine sensor GRABDA2m and promoted conditioned place preference. Our findings reveal that ChIs acting via nAChRs transiently limit the reactivation of dopamine axons for subsequent action potentials in dopamine neurons and therefore generate a dynamic inverse scaling of dopamine release according to ChI activity.
Distinct representations of economic variables across regions and projections of the frontal cortex.
Economic decision-making requires evaluating information about available options, such as their expected value and economic risk. Previous studies have shown that frontal cortical neurons encode these variables, but how this encoding is structured across different frontal regions and projection pathways remains unclear. We developed a decision-making task for head-fixed mice in which we varied the expected value and risk associated with reward-predicting stimuli. Using large-scale electrophysiology, two-photon imaging, and projection-specific optotagging, we identified distinct spatial gradients for these variables, with stronger expected value coding in dorsal frontal regions and stronger risk coding in medial regions. We then demonstrated that this encoding further depends on the neuronal projections: frontal neurons projecting to the dorsomedial striatum and claustrum differentially encoded economic variables. Our findings illustrate that frontal cortical representation of economic variables is jointly determined by spatial organization and downstream connectivity of neurons, revealing a structured, multi-scale code for economic variables.
Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex.
Every decision we make is accompanied by a sense of confidence about its likely outcome. This sense informs subsequent behavior, such as investing more-whether time, effort, or money-when reward is more certain. A neural representation of confidence should originate from a statistical computation and predict confidence-guided behavior. An additional requirement for confidence representations to support metacognition is abstraction: they should emerge irrespective of the source of information and inform multiple confidence-guided behaviors. It is unknown whether neural confidence signals meet these criteria. Here, we show that single orbitofrontal cortex neurons in rats encode statistical decision confidence irrespective of the sensory modality, olfactory or auditory, used to make a choice. The activity of these neurons also predicts two confidence-guided behaviors: trial-by-trial time investment and cross-trial choice strategy updating. Orbitofrontal cortex thus represents decision confidence consistent with a metacognitive process that is useful for mediating confidence-guided economic decisions.
Temporal regularities shape perceptual decisions and striatal dopamine signals.
Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that male mice adapt their perceptual choice history biases to different temporal regularities of visual stimuli. This adaptation was slow, evolving over hundreds of trials across several days. It occurred alongside a fast non-adaptive choice history bias, limited to a few trials. Both fast and slow trial history effects are well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that dorsal striatal dopamine tracks predictions of the model and behavior, suggesting that striatal dopamine reports reward predictions associated with adaptive choice history biases. Our results reveal the adaptive nature of perceptual choice history biases and shed light on their underlying computational principles and neural correlates.
Long Term Recordings with Immobile Silicon Probes in the Mouse Cortex.
A key experimental approach in neuroscience involves measuring neuronal activity in behaving animals with extracellular chronic recordings. Such chronic recordings were initially made with single electrodes and tetrodes, and are now increasingly performed with high-density, high-count silicon probes. A common way to achieve long-term chronic recording is to attach the probes to microdrives that progressively advance them into the brain. Here we report, however, that such microdrives are not strictly necessary. Indeed, we obtained high-quality recordings in both head-fixed and freely moving mice for several months following the implantation of immobile chronic probes. Probes implanted into the primary visual cortex yielded well-isolated single units whose spike waveform and orientation tuning were highly reproducible over time. Although electrode drift was not completely absent, stable waveforms occurred in at least 70% of the neurons tested across consecutive days. Thus, immobile silicon probes represent a straightforward and reliable technique to obtain stable, long-term population recordings in mice, and to follow the activity of populations of well-isolated neurons over multiple days.
Learning not to feel: reshaping the resolution of tactile perception.
We asked whether biased feedback during training could cause human subjects to lose perceptual acuity in a vibrotactile frequency discrimination task. Prior to training, we determined each subject's vibration frequency discrimination capacity on one fingertip, the Just Noticeable Difference (JND). Subjects then received 850 trials in which they performed a same/different judgment on two vibrations presented to that fingertip. They gained points whenever their judgment matched the computer-generated feedback on that trial. Feedback, however, was biased: the probability per trial of "same" feedback was drawn from a normal distribution with standard deviation twice as wide as the subject's JND. After training, the JND was significantly widened: stimulus pairs previously perceived as different were now perceived as the same. The widening of the JND extended to the untrained hand, indicating that the decrease in resolution originated in non-topographic brain regions. In sum, the acuity of subjects' sensory-perceptual systems shifted in order to match the feedback received during training.
Attention during adaptation weakens negative afterimages of perceptually colour-spread surfaces.
The visual system can complete coloured surfaces from stimulus fragments, inducing the subjective perception of a colour-spread figure. Negative afterimages of these induced colours were first reported by S. Shimojo, Y. Kamitani, and S. Nishida (2001). Two experiments were conducted to examine the effect of attention on the duration of these afterimages. The results showed that shifting attention to the colour-spread figure during the adaptation phase weakened the subsequent afterimage. On the basis of previous findings that the duration of these afterimages is correlated with the strength of perceptual filling-in (grouping) among local inducers during the adaptation phase, it is proposed that attention weakens perceptual filling-in during the adaptation phase and thereby prevents the stimulus from being segmented into an illusory figure.
Enhanced response of neurons in rat somatosensory cortex to stimuli containing temporal noise.
Sensory stimuli under natural conditions often consist of a temporally irregular sequence of events, contrasting with the periodic sequences commonly used as stimuli in the laboratory. These experiments compared the responses of neurons in rat barrel cortex with trains of whisker movements with different frequencies; each train possessed either a periodic or an irregular, "noisy" temporal structure. Periodic stimulus trains were composed of a sequence of 21 whisker deflections separated by 20 equal interdeflection intervals (IDIs). Noisy trains were matched for mean IDI but included intervals shorter and longer than the mean IDI. Cortical responses were equivalent for periodic and noisy stimuli for frequencies up to 10 Hz. Above 10 Hz, temporal noise led to a larger response magnitude, and this effect was amplified as deflection frequency increased. Noise also caused a sharpening of the temporal precision of response to the individual deflections of the stimulus train. Cortical neurons thus appear to be "tuned" to respond in a different way to stimuli characterized by temporal unpredictability. As a consequence, perceptual judgments that depend on somatosensory cortical firing rate may be affected by the presence of temporal noise.