Dopamine signals as temporal difference errors: recent advances

Enhanced cognitive flexibility and phasic striatal dopamine dynamics in a mouse model of low striatal tonic dopamine

The catecholamine neuromodulators dopamine and norepinephrine are implicated in motor function, motivation, and cognition. Although roles for striatal dopamine in these aspects of behavior are well established, the specific roles for cortical catecholamines in regulating striatal dopamine dynamics and behavior are less clear. We recently showed that elevating cortical dopamine but not norepinephrine suppresses hyperactivity in dopamine transporter knockout (DAT-KO) mice, which have elevated striatal dopamine levels. In contrast, norepinephrine transporter knockout (NET-KO) mice have a phenotype distinct from DAT-KO mice, as they show elevated extracellular cortical catecholamines but reduced baseline striatal dopamine levels. Here we evaluated the consequences of altered catecholamine levels in NET-KO mice on cognitive flexibility and striatal dopamine dynamics. In a probabilistic reversal learning task, NET-KO mice showed enhanced reversal learning, which was consistent with larger phasic dopamine transients (dLight) in the dorsomedial striatum (DMS) during reward delivery and reward omission, compared to WT controls. Selective depletion of dorsal medial prefrontal cortex (mPFC) norepinephrine in WT mice did not alter performance on the reversal learning task but reduced nestlet shredding. Surprisingly, NET-KO mice did not show altered breakpoints in a progressive ratio task, suggesting intact food motivation. Collectively, these studies show novel roles of cortical catecholamines in the regulation of tonic and phasic striatal dopamine dynamics and cognitive flexibility, updating our current views on dopamine regulation and informing future therapeutic strategies to counter multiple psychiatric disorders.

Explaining dopamine through prediction errors and beyond

The most influential account of phasic dopamine holds that it reports reward prediction errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form, probably too simple and fails to explain all the properties of phasic dopamine observed in behaving animals. This Perspective helps to resolve some of the conflicting interpretations of dopamine that currently exist in the literature. We focus on the following three empirical challenges to the RPE theory of dopamine: why does dopamine (1) ramp up as animals approach rewards, (2) respond to sensory and motor features and (3) influence action selection? We argue that the prediction error concept, once it has been suitably modified and generalized based on an analysis of each computational problem, answers each challenge. Nonetheless, there are a number of additional empirical findings that appear to demand fundamentally different theoretical explanations beyond encoding RPE. Therefore, looking forward, we discuss the prospects for a unifying theory that respects the diversity of dopamine signaling and function as well as the complex circuitry that both underlies and responds to dopaminergic transmission.

Value-Driven Adaptations of Mesolimbic Dopamine Release Are Governed by Both Model-Based and Model-Free Mechanisms

The magnitude of dopamine signals elicited by rewarding events and their predictors is updated when reward value changes. It is actively debated how readily these dopamine signals adapt and whether adaptation aligns with model-free or model-based reinforcement-learning principles. To investigate this, we trained male rats in a pavlovian-conditioning paradigm and measured dopamine release in the nucleus accumbens core in response to food reward (unconditioned stimulus) and reward-predictive conditioned stimuli (CS), both before and after reward devaluation, induced via either sensory-specific or nonspecific satiety. We demonstrate that (1) such devaluation reduces CS-induced dopamine release rapidly, without additional pairing of CS with devalued reward and irrespective of whether the devaluation was sensory-specific or nonspecific. In contrast, (2) reward devaluation did not decrease food reward-induced dopamine release. Surprisingly, (3) postdevaluation reconditioning, by additional pairing of CS with devalued reward, rapidly reinstated CS-induced dopamine signals to predevaluation levels. Taken together, we identify distinct, divergent adaptations in dopamine-signal magnitude when reward value is decreased: CS dopamine diminishes but reinstates fast, whereas reward dopamine is resistant to change. This implies that, respective to abovementioned findings, (1) CS dopamine may be governed by a model-based mechanism and (2) reward dopamine by a model-free one, where (3) the latter may contribute to swift reinstatement of the former. However, changes in CS dopamine were not selective for sensory specificity of reward devaluation, which is inconsistent with model-based processes. Thus, mesolimbic dopamine signaling incorporates both model-free and model-based mechanisms and is not exclusively governed by either.

Reward prediction error neurons implement an efficient code for reward

We use efficient coding principles borrowed from sensory neuroscience to derive the optimal neural population to encode a reward distribution. We show that the responses of dopaminergic reward prediction error neurons in mouse and macaque are similar to those of the efficient code in the following ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions and lower slopes; and their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to the efficient code. The learning rule for the position of the neuron on the reward axis closely resembles distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.

More widespread and rigid neuronal representation of reward expectation underlies impulsive choices

Impulsive choices prioritize smaller, more immediate rewards over larger, delayed, or potentially uncertain rewards. Impulsive choices are a critical aspect of substance use disorders and maladaptive decision-making across the lifespan. Here, we sought to understand the neuronal underpinnings of expected reward and risk estimation on a trial-by-trial basis during impulsive choices. To do so, we acquired electrical recordings from the human brain while participants carried out a risky decision-making task designed to measure choice impulsivity. Behaviorally, we found a reward-accuracy tradeoff, whereby more impulsive choosers were more accurate at the task, opting for a more immediate reward while compromising overall task performance. We then examined how neuronal populations across frontal, temporal, and limbic brain regions parametrically encoded reinforcement learning model variables, namely reward and risk expectation and surprise, across trials. We found more widespread representations of reward value expectation and prediction error in more impulsive choosers, whereas less impulsive choosers preferentially represented risk expectation. A regional analysis of reward and risk encoding highlighted the anterior cingulate cortex for value expectation, the anterior insula for risk expectation and surprise, and distinct regional encoding between impulsivity groups. Beyond describing trial-by-trial population neuronal representations of reward and risk variables, these results suggest impaired inhibitory control and model-free learning underpinnings of impulsive choice. These findings shed light on neural processes underlying reinforced learning and decision-making in uncertain environments and how these processes may function in psychiatric disorders.

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum - a signal implicated in associative learning - in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a novel causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate inter-trial-interval (ITI) state representation. Recurrent neural networks trained within a TD framework develop state representations like our best 'handcrafted' model. Our findings suggest that the TD error can be a measure that describes both contingency and dopaminergic activity.

Slow-rising and fast-falling dopaminergic dynamics jointly adjust negative prediction error in the ventral striatum

The greater the reward expectations are, the more different the brain's physiological response will be. Although it is well-documented that better-than-expected outcomes are encoded quantitatively via midbrain dopaminergic (DA) activity, it has been less addressed experimentally whether worse-than-expected outcomes are expressed quantitatively as well. We show that larger reward expectations upon unexpected reward omissions are associated with the preceding slower rise and following larger decrease (DA dip) in the DA concentration at the ventral striatum of mice. We set up a lever press task on a fixed ratio (FR) schedule requiring five lever presses as an effort for a food reward (FR5). The mice occasionally checked the food magazine without a reward before completing the task. The percentage of this premature magazine entry (PME) increased as the number of lever presses approached five, showing rising expectations with increasing proximity to task completion, and hence greater reward expectations. Fibre photometry of extracellular DA dynamics in the ventral striatum using a fluorescent protein (genetically encoded GPCR activation-based DA sensor: GRABDA2m ) revealed that the slow increase and fast decrease in DA levels around PMEs were correlated with the PME percentage, demonstrating a monotonic relationship between the DA dip amplitude and degree of expectations. Computational modelling of the lever press task implementing temporal difference errors and state transitions replicated the observed correlation between the PME frequency and DA dip amplitude in the FR5 task. Taken together, these findings indicate that the DA dip amplitude represents the degree of reward expectations monotonically, which may guide behavioural adjustment.

Mobilization of endocannabinoids by midbrain dopamine neurons is required for the encoding of reward prediction

Brain levels of the endocannabinoid 2-arachidonoylglycerol (2-AG) shape motivated behavior and nucleus accumbens (NAc) dopamine release. However, it is not clear whether mobilization of 2-AG specifically from midbrain dopamine neurons is necessary for dopaminergic responses to external stimuli predicting forthcoming reward. Here, we use a viral-genetic strategy to prevent the expression of the 2-AG-synthesizing enzyme diacylglycerol lipase α (DGLα) from ventral tegmental area (VTA) dopamine cells in adult mice. We find that DGLα deletion from VTA dopamine neurons prevents depolarization-induced suppression of excitation (DSE), a form of 2-AG-mediated synaptic plasticity, in dopamine neurons. DGLα deletion also decreases effortful, cue-driven reward-seeking but has no effect on non-cued or low-effort operant tasks and other behaviors. Moreover, dopamine recording in the NAc reveals that deletion of DGLα impairs the transfer of accumbal dopamine signaling from a reward to its earliest predictors. These results demonstrate that 2-AG mobilization from VTA dopamine neurons is a necessary step for the generation of dopamine-based predictive associations that are required to direct and energize reward-oriented behavior.

Neuronal implementation of the temporal difference learning algorithm in the midbrain dopaminergic system

The temporal difference learning (TDL) algorithm has been essential to conceptualizing the role of dopamine in reinforcement learning (RL). Despite its theoretical importance, it remains unknown whether a neuronal implementation of this algorithm exists in the brain. Here, we provide an interpretation of the recently described signaling properties of ventral tegmental area (VTA) GABAergic neurons and show that a circuitry of these neurons implements the TDL algorithm. Specifically, we identified the neuronal mechanism of three key components of the TDL model: a sustained state value signal encoded by an afferent input to the VTA, a temporal differentiation circuit formed by two types of VTA GABAergic neurons the combined output of which computes momentary reward prediction (RP) as the derivative of the state value, and the computation of reward prediction errors (RPEs) in dopamine neurons utilizing the output of the differentiation circuit. Using computational methods, we also show that this mechanism is optimally adapted to the biophysics of RPE signaling in dopamine neurons, mechanistically links the emergence of conditioned reinforcement to RP, and can naturally account for the temporal discounting of reinforcement. Elucidating the implementation of the TDL algorithm may further the investigation of RL in biological and artificial systems.

Reframing dopamine: A controlled controller at the limbic-motor interface

Pavlovian influences notoriously interfere with operant behaviour. Evidence suggests this interference sometimes coincides with the release of the neuromodulator dopamine in the nucleus accumbens. Suppressing such interference is one of the targets of cognitive control. Here, using the examples of active avoidance and omission behaviour, we examine the possibility that direct manipulation of the dopamine signal is an instrument of control itself. In particular, when instrumental and Pavlovian influences come into conflict, dopamine levels might be affected by the controlled deployment of a reframing mechanism that recasts the prospect of possible punishment as an opportunity to approach safety, and the prospect of future reward in terms of a possible loss of that reward. We operationalize this reframing mechanism and fit the resulting model to rodent behaviour from two paradigmatic experiments in which accumbens dopamine release was also measured. We show that in addition to matching animals' behaviour, the model predicts dopamine transients that capture some key features of observed dopamine release at the time of discriminative cues, supporting the idea that modulation of this neuromodulator is amongst the repertoire of cognitive control strategies.

Expectancy-related changes in firing of dopamine neurons depend on hippocampus

The orbitofrontal cortex (OFC) and hippocampus (HC) are both implicated in forming the cognitive or task maps that support flexible behavior. Previously, we used the dopamine neurons as a sensor or tool to measure the functional effects of OFC lesions (Takahashi et al., 2011). We recorded midbrain dopamine neurons as rats performed an odor-based choice task, in which errors in the prediction of reward were induced by manipulating the number or timing of the expected rewards across blocks of trials. We found that OFC lesions ipsilateral to the recording electrodes caused prediction errors to be degraded consistent with a loss in the resolution of the task states, particularly under conditions where hidden information was critical to sharpening the predictions. Here we have repeated this experiment, along with computational modeling of the results, in rats with ipsilateral HC lesions. The results show HC also shapes the map of our task, however unlike OFC, which provides information local to the trial, the HC appears to be necessary for estimating the upper-level hidden states based on the information that is discontinuous or separated by longer timescales. The results contrast the respective roles of the OFC and HC in cognitive mapping and add to evidence that the dopamine neurons access a rich information set from distributed regions regarding the predictive structure of the environment, potentially enabling this powerful teaching signal to support complex learning and behavior.

Roles of octopamine neurons in the vertical lobe of the mushroom body for the execution of a conditioned response in cockroaches

Aminergic neurons mediate reward signals in mammals and insects. In crickets, we showed that blockade of synaptic transmission from octopamine neurons (OANs) impairs conditioning of an odor (conditioned stimulus, CS) with water or sucrose (unconditioned stimulus, US) and execution of a conditioned response (CR) to the CS. It has not yet been established, however, whether findings in crickets can be applied to other species of insects. In this study, we investigated the roles of OANs in conditioning of salivation, monitored by activities of salivary neurons, and in execution of the CR in cockroaches (Periplaneta americana). We showed that injection of epinastine (an OA receptor antagonist) into the head hemolymph impaired both conditioning and execution of the CR, in accordance with findings in crickets. Moreover, local injection of epinastine into the vertical lobes of the mushroom body (MB), the center for associative learning and control of the CR, impaired execution of the CR, whereas injection of epinastine into the calyces of the MB or the antennal lobes (primary olfactory centers) did not. We propose that OANs in the MB vertical lobes play critical roles in the execution of the CR in cockroaches. This is analogous to the fact that midbrain dopamine neurons govern execution of learned actions in mammals.

Dopaminergic prediction errors in the ventral tegmental area reflect a multithreaded predictive model

Dopamine neuron activity is tied to the prediction error in temporal difference reinforcement learning models. These models make significant simplifying assumptions, particularly with regard to the structure of the predictions fed into the dopamine neurons, which consist of a single chain of timepoint states. Although this predictive structure can explain error signals observed in many studies, it cannot cope with settings where subjects might infer multiple independent events and outcomes. In the present study, we recorded dopamine neurons in the ventral tegmental area in such a setting to test the validity of the single-stream assumption. Rats were trained in an odor-based choice task, in which the timing and identity of one of several rewards delivered in each trial changed across trial blocks. This design revealed an error signaling pattern that requires the dopamine neurons to access and update multiple independent predictive streams reflecting the subject's belief about timing and potentially unique identities of expected rewards.

Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective 'teacher' by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the 'student' compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

Ventral tegmental area integrity measured with high-resolution 7-Tesla MRI relates to motivation across depression and anxiety diagnoses

The ventral tegmental area (VTA) is one of the major sources of dopamine in the brain and has been associated with reward prediction, error-based reward learning, volitional drive and anhedonia. However, precise anatomical investigations of the VTA have been prevented by the use of standard-resolution MRI, reliance on subjective manual tracings, and lack of quantitative measures of dopamine-related signal. Here, we combine ultra-high field 400 µm³ quantitative MRI with dopamine-related signal mapping, and a mixture of machine learning and supervised computational techniques to delineate the VTA in a transdiagnostic sample of subjects with and without depression and anxiety disorders. Subjects also underwent cognitive testing to measure intrinsic and extrinsic motivational tone. Fifty-one subjects were scanned in total, including healthy control (HC) and mood/anxiety (MA) disorder subjects. MA subjects had significantly larger VTA volumes compared to HC but significantly lower signal intensity within VTA compared to HC, indicating reduced structural integrity of the dopaminergic VTA. Interestingly, while VTA integrity did not significantly correlate with self-reported depression or anxiety symptoms, it was correlated with an objective cognitive measure of extrinsic motivation, whereby lower VTA integrity was associated with lower motivation. This is the first study to demonstrate a computational pipeline for detecting and delineating the VTA in human subjects with 400 μm³ resolution. We highlight the use of objective transdiagnostic measures of cognitive function that link neural integrity to behavior across clinical and non-clinical groups.

The Development of the Mesoprefrontal Dopaminergic System in Health and Disease

Midbrain dopaminergic neurons located in the substantia nigra and the ventral tegmental area are the main source of dopamine in the brain. They send out projections to a variety of forebrain structures, including dorsal striatum, nucleus accumbens, and prefrontal cortex (PFC), establishing the nigrostriatal, mesolimbic, and mesoprefrontal pathways, respectively. The dopaminergic input to the PFC is essential for the performance of higher cognitive functions such as working memory, attention, planning, and decision making. The gradual maturation of these cognitive skills during postnatal development correlates with the maturation of PFC local circuits, which undergo a lengthy functional remodeling process during the neonatal and adolescence stage. During this period, the mesoprefrontal dopaminergic innervation also matures: the fibers are rather sparse at prenatal stages and slowly increase in density during postnatal development to finally reach a stable pattern in early adulthood. Despite the prominent role of dopamine in the regulation of PFC function, relatively little is known about how the dopaminergic innervation is established in the PFC, whether and how it influences the maturation of local circuits and how exactly it facilitates cognitive functions in the PFC. In this review, we provide an overview of the development of the mesoprefrontal dopaminergic system in rodents and primates and discuss the role of altered dopaminergic signaling in neuropsychiatric and neurodevelopmental disorders.

Dopamine Transporter Is a Master Regulator of Dopaminergic Neural Network Connectivity

Dopaminergic neurons of the substantia nigra pars compacta (SNC) and ventral tegmental area (VTA) exhibit spontaneous firing activity. The dopaminergic neurons in these regions have been shown to exhibit differential sensitivity to neuronal loss and psychostimulants targeting dopamine transporter. However, it remains unclear whether these regional differences scale beyond individual neuronal activity to regional neuronal networks. Here, we used live-cell calcium imaging to show that network connectivity greatly differs between SNC and VTA regions with higher incidence of hub-like neurons in the VTA. Specifically, the frequency of hub-like neurons was significantly lower in SNC than in the adjacent VTA, consistent with the interpretation of a lower network resilience to SNC neuronal loss. We tested this hypothesis, in DAT-cre/loxP-GCaMP6f mice of either sex, when activity of an individual dopaminergic neuron is suppressed, through whole-cell patch clamp electrophysiology, in either SNC or VTA networks. Neuronal loss in the SNC increased network clustering, whereas the larger number of hub-neurons in the VTA overcompensated by decreasing network clustering in the VTA. We further show that network properties are regulatable via a dopamine transporter but not a D2 receptor dependent mechanism. Our results demonstrate novel regulatory mechanisms of functional network topology in dopaminergic brain regions.SIGNIFICANCE STATEMENT In this work, we begin to untangle the differences in complex network properties between the substantia nigra pars compacta (SNC) and VTA, that may underlie differential sensitivity between regions. The methods and analysis employed provide a springboard for investigations of network topology in multiple deep brain structures and disorders.

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.