Dopamine neuron ensembles signal the content of sensory prediction errors

Stalnaker, Thomas A.; Howard, James D.; Takahashi, Yuji K.; Gershman, Samuel J.; Kahnt, Thorsten; Schoenbaum, Geoffrey

doi:10.1101/723908

Cited by 9 publications

(16 citation statements)

References 42 publications

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“…Thus, a single reward could elicit a different response in each DA neuron since each of them codes its own value prediction. Similarly, it has been proposed that sensory prediction error is not represented by individual DA neurons but rather encoded in the firing pattern of many DA neurons (Stalnaker et al, 2019). Thus, the diversity in responses observed here would simply reflect the necessity of a multidimensional encoding of prediction error to handle a self-paced complex operant task.…”

Section: Cue and Reward Codingsupporting

confidence: 49%

Context-Dependent Multiplexing by Individual VTA Dopamine Neurons

et al. 2020

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Dopamine (DA) neurons of the VTA track cues and rewards to generate a reward prediction error signal during Pavlovian conditioning. Here we explored how these neurons respond to a self-paced, operant task in freely moving mice. The animal could trigger a reward-predicting cue by remaining in a specific location of an operant box for a brief time before moving to a spout for reward collection. VTA DA neurons were identified using DAT-Cre male mice that carried an optrode with minimal impact on the behavioral task. In vivo single-unit recordings revealed transient fast spiking responses to the cue and reward in correct trials, while for incorrect ones the activity paused, reflecting positive and negative error signals of a reward prediction. In parallel, a majority of VTA DA neurons simultaneously encoded multiple actions (e.g., movement velocity, acceleration, distance to goal, and licking) in sustained slow firing modulation. Applying a GLM, we show that such multiplexed encoding of rewarding and motor variables by individual DA neurons was only apparent while the mouse was engaged in the task. Downstream targets may exploit such goal-directed multiplexing of VTA DA neurons to adjust actions to optimize the task's outcome.

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Section: Cue and Reward Codingsupporting

confidence: 49%

Context-Dependent Multiplexing by Individual VTA Dopamine Neurons

et al. 2020

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“…However, recent studies have shown that this dopamine error acts more like a teaching signal to instruct humans and other animals to associate events together (e.g., stimulus-reward or stimulusstimulus associations), regardless of whether either of those events contain something valuable or rewarding, and without endowing those events with value [33-35, 39-42, 118]. Further, dopamine errors in both humans and rodents contain information about predicted rewards [42], suggesting this signal serves to instruct neural regions on what to learn about, as well as when to learn [35]. This demonstrates that the dopamine prediction error does not act as a homogenous signal that broadcasts the value or salience of an event (or even allocations of lasting attention to a stimulus [43]), but as a teaching signal that is received throughout the brain to drive learnt associations that two constructs in the world are related (Fig.…”

Section: Dopamine: a Complex System Subserving Many Different Forms Of Reinforcement Learningmentioning

confidence: 99%

“…Further, optogenetic inhibition designed to silence the dopamine prediction error across the transition between two neutral sensory stimuli, reduces the association between such stimuli [39], suggesting a physiological role for dopamine in the development of these associations. The second line of evidence supporting a role for dopamine in instructing learned associations comes from recording of neural activity in VTA (or nucleus accumbens) of mice, rats, and humans [42,119,120]. For instance, patterns of firing across ensembles of midbrain dopamine neurons have been shown to contain identity information of sensory prediction errors [42].…”

Section: Dopamine: a Complex System Subserving Many Different Forms Of Reinforcement Learningmentioning

confidence: 99%

“…The second line of evidence supporting a role for dopamine in instructing learned associations comes from recording of neural activity in VTA (or nucleus accumbens) of mice, rats, and humans [42,119,120]. For instance, patterns of firing across ensembles of midbrain dopamine neurons have been shown to contain identity information of sensory prediction errors [42]. In addition, exciting new research has reported the presence of wave-like spatiotemporal dopamine dynamics in the dorsal striatum, with the propagation of specific wave trajectories (i.e., whether waves propagated from medial to lateral dorsal striatum, or lateral to medial) dependant on the demands of a learning task [119].…”

Section: Dopamine: a Complex System Subserving Many Different Forms Of Reinforcement Learningmentioning

confidence: 99%

“…Specifically, this research has demonstrated that we can no longer explain phasic dopamine signalling as a homogenous signal that broadcasts salience or value of a current event [32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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The prediction-error hypothesis of schizophrenia: new data point to circuit-specific changes in dopamine activity

Millard

Bearden

Karlsgodt

et al. 2021

Neuropsychopharmacol.

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Schizophrenia is a severe psychiatric disorder affecting 21 million people worldwide. People with schizophrenia suffer from symptoms including psychosis and delusions, apathy, anhedonia, and cognitive deficits. Strikingly, schizophrenia is characterised by a learning paradox involving difficulties learning from rewarding events, whilst simultaneously ‘overlearning’ about irrelevant or neutral information. While dysfunction in dopaminergic signalling has long been linked to the pathophysiology of schizophrenia, a cohesive framework that accounts for this learning paradox remains elusive. Recently, there has been an explosion of new research investigating how dopamine contributes to reinforcement learning, which illustrates that midbrain dopamine contributes in complex ways to reinforcement learning, not previously envisioned. This new data brings new possibilities for how dopamine signalling contributes to the symptomatology of schizophrenia. Building on recent work, we present a new neural framework for how we might envision specific dopamine circuits contributing to this learning paradox in schizophrenia in the context of models of reinforcement learning. Further, we discuss avenues of preclinical research with the use of cutting-edge neuroscience techniques where aspects of this model may be tested. Ultimately, it is hoped that this review will spur to action more research utilising specific reinforcement learning paradigms in preclinical models of schizophrenia, to reconcile seemingly disparate symptomatology and develop more efficient therapeutics.

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