Biology subjects

Watabe-Uchida, M.

Publications and source records attributed to Watabe-Uchida, M..

11 recordsLinked to original sources

bioRxiv2026

Dopamine in the ventral and tail of striatum supports global and local evaluation in reward-threat conflict

Survival requires balancing reward seeking and threat avoidance, yet how distinct dopamine systems coordinate to support this remains unclear. Using a naturalistic foraging paradigm in which mice pursue water reward under threat from a monster object, we examined roles of dopamine projections to the ventral striatum (VS) and tail of the striatum (TS). Ablation of VS- projecting dopamine neurons impaired both distal reward pursuit and threat avoidance, with the impairment in threat avoidance paralleling effects of TS dopamine ablation. However, simultaneous recordings revealed different activity rules: VS dopamine tracked radial velocity towards the current goal as animals changed goals (reward or shelter), consistent with a temporal-difference error of spatial value, while TS dopamine encoded proximity and orientation to the threat, reflecting immediate sensory experience. Taken together, VS and TS dopamine evaluates distinct state information for avoidance. VS dopamine facilitates allocentric, goal- directed navigation, while TS dopamine facilitates egocentric, stimulus-driven threat responses.

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bioRxiv2025

A hardwired neural circuit for temporal difference learning

The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brains predictions about rewards. A leading theory proposes that this process is analogous to a reinforcement learning algorithm called temporal difference (TD) learning, and that dopamine acts as the error term within the TD algorithm (TD error). Although many studies have demonstrated similarities between dopamine activity and TD errors1-5, the mechanistic basis for dopaminergic TD learning remains unknown. Here, we combined large-scale neural recordings with patterned optogenetic stimulation to examine whether and how the key steps in TD learning are accomplished by the circuitry connecting dopamine neurons and their targets. Replacing natural rewards with optogenetic stimulation of dopamine axons in the nucleus accumbens (NAc) in a classical conditioning task gradually generated TD error-like activity patterns in dopamine neurons by specifically modifying the task-related activity of NAc neurons expressing the D1 dopamine receptor (D1 neurons). In turn, patterned optogenetic stimulation of NAc D1 neurons in naive animals drove dopamine neuron spiking according to the TD error of the stimulation pattern, indicating that TD computations are hardwired into this circuit. The transformation from D1 neurons to dopamine neurons could be described by a biphasic linear filter, with a rapid positive and delayed negative phase, that effectively computes a temporal difference. This finding suggests that the time horizon over which the TD algorithm operates--the temporal discount factor--is set by the balance of the positive and negative components of the linear filter, pointing to a circuit-level mechanism for temporal discounting. These results provide a new conceptual framework for understanding how the computations and parameters governing animal learning arise from neurobiological components.

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bioRxiv2024

Hunger modulates exploration through suppression of dopamine signaling in the tail of striatum

Caloric depletion leads to behavioral changes that help an animal find food and restore its homeostatic balance. Hunger increases exploration and risk-taking behavior, allowing an animal to forage for food despite risks; however, the neural circuitry underlying this change is unknown. Here, we characterize how hunger restructures an animals spontaneous behavior as well as its directed exploration of a novel object. We show that hunger-induced changes in exploration are accompanied by and result from modulation of dopamine signaling in the tail of the striatum (TOS). Dopamine signaling in the TOS is modulated by internal hunger state through the activity of agouti-related peptide (AgRP) neurons, putative "hunger neurons" in the arcuate nucleus of the hypothalamus. These AgRP neurons are poly-synaptically connected to TOS-projecting dopaminergic neurons through the lateral hypothalamus, the central amygdala, and the periaqueductal grey. We thus delineate a hypothalamic-midbrain circuit that coordinates changes in exploration behavior in the hungry state.

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bioRxiv2023

Glutamate inputs send prediction error of reward but not negative value of aversive stimuli to dopamine neurons

Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs) but the mechanisms underlying RPE computation, particularly contributions of different neurotransmitters, remain poorly understood. Here we used a genetically-encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons. We found that glutamate inputs exhibit virtually all of the characteristics of RPE, rather than conveying a specific component of RPE computation such as reward or expectation. Notably, while glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli toward more positive responses, while excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.

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bioRxiv2023

Improved dual-color GRAB sensors for monitoring dopaminergic activity in vivo

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a vast network of dopaminergic projections. To fully dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent GPCR activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity, and signal-to-noise properties with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in freely moving mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala, and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

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bioRxiv2023

A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis

Social grouping increases survival in many species, including humans1,2. By contrast, social isolation generates an aversive state (loneliness) that motivates social seeking and heightens social interaction upon reunion3-5. The observed rebound in social interaction triggered by isolation suggests a homeostatic process underlying the control of social drive, similar to that observed for physiological needs such as hunger, thirst or sleep3,6. In this study, we assessed social responses in multiple mouse strains and identified the FVB/NJ line as exquisitely sensitive to social isolation. Using FVB/NJ mice, we uncovered two previously uncharacterized neuronal populations in the hypothalamic preoptic nucleus that are activated during social isolation and social rebound and that orchestrate the behavior display of social need and social satiety, respectively. We identified direct connectivity between these two populations of opposite function and with brain areas associated with social behavior, emotional state, reward, and physiological needs, and showed that animals require touch to assess the presence of others and fulfill their social need, thus revealing a brain-wide neural system underlying social homeostasis. These findings offer mechanistic insight into the nature and function of circuits controlling instinctive social need and for the understanding of healthy and diseased brain states associated with social context.

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bioRxiv2022

Dynamical management of potential threats regulated by dopamine and direct- and indirect-pathway neurons in the tail of the striatum

Avoiding potential threats before experiencing an actual outcome is critical to prevent a disaster. Here we examined roles of the tail of the striatum (TS) and its dopamine input in threat management. Mice were presented with a potential threat (a moving object) while pursuing rewards. Mice initially failed to obtain rewards, but gradually successfully obtained rewards in later trials. We show that the initial failures depended on dopamine and direct-pathway neurons in TS, and variability in failure rate across trials and individuals was positively correlated with the activity of these neurons. In contrast, indirect-pathway neurons in TS were critical for eventual improvement in reward acquisition, and their activity was positively correlated with successful reward acquisition. These results demonstrate that direct- and indirect-pathway TS neurons promote and suppress threat avoidance, respectively, at different stages, providing a mechanism for overcoming a potential threat while maintaining the threat estimates.

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bioRxiv2021

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Animals exhibit diverse behavioral responses, such as exploration and avoidance, to novel cues in the environment. However, it remains unclear how dopamine neuron-related novelty responses influence behavior. Here, we characterized dynamics of novelty exploration using multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq). Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability were explained by a novel reinforcement learning (RL) model of threat prediction, in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus"), and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems. HighlightsO_LINovelty-induced behaviors are analyzed using modern machine-learning methods C_LIO_LINovelty induces risk assessment which develops into engagement or avoidance C_LIO_LIDopamine in the tail of striatum correlates with individual behavioral variability C_LIO_LIReinforcement learning with shaping bonus and uncertainty explains the data C_LI

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bioRxiv2020

A gradual backward shift of dopamine responses during associative learning

It has been proposed that the activity of dopamine neurons approximates temporal difference (TD) prediction error, a teaching signal developed in reinforcement learning, a field of machine learning. However, whether this similarity holds true during learning remains elusive. In particular, some TD learning models predict that the error signal gradually shifts backward in time from reward delivery to a reward-predictive cue, but previous experiments failed to observe such a gradual shift in dopamine activity. Here we demonstrate conditions in which such a shift can be detected experimentally. These shared dynamics of TD error and dopamine activity narrow the gap between machine learning theory and biological brains, tightening a long-sought link.

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bioRxiv2020

Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task

Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed in a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, i.e. the temporal difference error. A major difference between these areas was the overall activity level of reward responses: reward responses in dorsolateral striatum (DLS) were positively shifted, lacking inhibitory responses to negative prediction error. Tenets of habit and skill can be explained by this positively biased dopamine signal in DLS.

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bioRxiv2019

A unified framework for dopamine signals across timescales

Rapid phasic activity of midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. Recent studies describing slowly increasing dopamine signals have instead proposed that they represent state values and arise independently from somatic spiking activity. Here, we developed novel experimental paradigms using virtual reality that disambiguate RPEs from values. We examined the dopamine circuit activity at various stages including somatic spiking, axonal calcium signals, and striatal dopamine concentrations. Our results demonstrate that ramping dopamine signals are consistent with RPEs rather than value, and this ramping is observed at all the stages examined. We further show that ramping dopamine signals can be driven by a dynamic stimulus that indicates a gradual approach to a reward. We provide a unified computational understanding of rapid phasic and slowly ramping dopamine signals: dopamine neurons perform a derivative-like computation over values on a moment-by-moment basis.

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