Reward Prediction Error Coding in Dorsal Striatal Neurons

Oyama, Kei; Hernádi, István; Iijima, Toshio; Tsutsui, Ken

doi:10.1523/jneurosci.1719-10.2010

Cited by 84 publications

(78 citation statements)

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“…Singleneuron recording studies in monkeys have also implicated the changes in activity of a particular class of striatal neurons, termed tonically active neurons (TANs) and thought to be cholinergic interneurons, as a possible neuronal substrate of the prediction error signal (Joshua et al, 2008;Apicella et al, 2009). However, as in the study by Oyama et al (2010), these findings were reported in Pavlovian (i.e., classical) conditioning in which no behavioral response is required for reward, whereas no evidence of prediction error signaling has been observed during an instrumental task in which the rewarding outcome depends on an action (Morris et al, 2004). Thus, an intriguing question is whether the processing of prediction error signals by TANs occurs preferentially in specific learning situations.…”

Section: Introductionmentioning

confidence: 58%

“…Functional brain imaging studies have identified prediction error-related activity in target structures of dopamine neurons, especially the striatum (Knutson et al, 2000;Pagnoni et al, 2002;McClure et al, 2003;O'Doherty et al, 2003O'Doherty et al, , 2004Schönberg et al, 2007). However, it remains unclear whether striatal neurons encode this type of prediction error (Schultz and Dickinson, 2000;Niv and Schoenbaum, 2008;Roesch et al, 2010), although it was recently reported that the activity of a group of striatal neurons in rats, presumed projection neurons, was linked to discrepancies between outcomes and their predictions, which suggests a coding of prediction error (Oyama et al, 2010). Singleneuron recording studies in monkeys have also implicated the changes in activity of a particular class of striatal neurons, termed tonically active neurons (TANs) and thought to be cholinergic interneurons, as a possible neuronal substrate of the prediction error signal (Joshua et al, 2008;Apicella et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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The Role of Striatal Tonically Active Neurons in Reward Prediction Error Signaling during Instrumental Task Performance

Apicella¹,

Ravel²,

Deffains³

et al. 2011

J. Neurosci.

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The detection of differences between predictions and actual outcomes is important for associative learning and for selecting actions according to their potential future reward. There are reports that tonically active neurons (TANs) in the primate striatum may carry information about errors in the prediction of rewards. However, this property seems to be expressed in classical conditioning tasks but not during performance of an instrumental task. To address this issue, we recorded the activity of TANs in the putamen of two monkeys performing an instrumental task in which probabilistic rewarding outcomes were contingent on an action in block-design experiments. Behavioral evidence suggests that animals adjusted their performance according to the level of probability for reward on each trial block. We found that the TAN response to reward was stronger as the reward probability decreased; this effect was especially prominent on the late component of the pause-rebound pattern of typical response seen in these neurons. The responsiveness to reward omission was also increased with increasing reward probability, whereas there were no detectable effects on responses to the stimulus that triggered the movement. Overall, the modulation of TAN responses by reward probability appeared relatively weak compared with that observed previously in a probabilistic classical conditioning task using the same block design. These data indicate that instrumental conditioning was less effective at demonstrating prediction error signaling in TANs. We conclude that the sensitivity of the TAN system to reward probability depends on the specific learning situation in which animals experienced the stimulus-reward associations.

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Section: Introductionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

The Role of Striatal Tonically Active Neurons in Reward Prediction Error Signaling during Instrumental Task Performance

Apicella¹,

Ravel²,

Deffains³

et al. 2011

J. Neurosci.

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“…In behavioral situations with contingencies changing about every 100 trials, dopamine neurons code the difference between current reward and reward history weighted by the last six to seven trials (Bayer et al, 2007). The occurrence of reward or reward prediction (positive prediction error) or their omission (negative prediction error) activates or depresses dopamine neurons in an inverse monotonic function of probability, such that the more unpredicted the event the stronger the response (de Lafuente and Romo, 2011;Enomoto et al, 2011;Fiorillo et al, 2003;Matsumoto and Hikosaka, 2009;Morris et al, 2006;Nakahara et al, 2004;Nomoto et al, 2010;Oyama et al, 2010;Satoh et al, 2003). Enomoto et al (2011) attempted to directly address whether the phasic dopamine response reflect the total future reward, as opposed to just the immediate reward.…”

Section: Phasic Dopamine Signals Represent Model-free Prediction Errorsmentioning

confidence: 99%

The role of learning-related dopamine signals in addiction vulnerability

Huys

Tobler

Hasler

et al. 2014

Progress in Brain Research

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Psychostimulants such as methylphenidate (MPH) and antidepressants such as fluoxetine (FLX) are widely used in the treatment of various mental disorders or as cognitive enhancers. These medications are often combined, for example, to treat comorbid disorders. There is a considerable body of evidence from animal models indicating that individually these psychotropic medications can have detrimental effects on the brain and behavior, especially when given during sensitive periods of brain development. However, almost no studies investigate possible interactions between these drugs. This is surprising given that their combined neurochemical effects (enhanced dopamine and serotonin neurotransmission) mimic some effects of illicit drugs such as cocaine and amphetamine. Here, we summarize recent studies in juvenile rats on the molecular effects in the mid-and forebrain and associated behavioral changes, after such combination treatments. Our findings indicate that these combined MPH + FLX treatments can produce similar molecular changes as seen after cocaine exposure while inducing behavioral changes indicative of dysregulated mood and motivation, effects that often endure into adulthood. Tables: 2 References: 254 ABSTRACT Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signalling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical and experimental accounts of phasic dopamine signalling, with implications for the role of learning-related dopamine signalling in addiction and related disorders. We describe the theoretical and behavioural characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction. CONTENTS

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“…Using such a task, Schultz and colleagues have found that dopamine neurons code reward prediction error in their phasic response to the stimulus and the reward and that they also code reward uncertainty in their tonic activity between the CS and the outcome (Fiorillo et al 2003). We have recently recorded single-unit activity in the rat dorsal striatum and SNc during a probabilistic Pavlovian conditioning task and found that a group of neurons in the dorsal striatum codes reward prediction error information in the same manner as dopamine neurons in the SNc (Oyama et al 2010). Whereas in that study we focused on the neurons coding reward prediction error, in this study we analyzed the same data set looking for any task-related variation of activity.…”

mentioning

confidence: 95%

Discrete coding of stimulus value, reward expectation, and reward prediction error in the dorsal striatum

Oyama

Tateyama

Hernádi

et al. 2015

Journal of Neurophysiology

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To investigate how the striatum integrates sensory information with reward information for behavioral guidance, we recorded single-unit activity in the dorsal striatum of head-fixed rats participating in a probabilistic Pavlovian conditioning task with auditory conditioned stimuli (CSs) in which reward probability was fixed for each CS but parametrically varied across CSs. We found that the activity of many neurons was linearly correlated with the reward probability indicated by the CSs. The recorded neurons could be classified according to their firing patterns into functional subtypes coding reward probability in different forms such as stimulus value, reward expectation, and reward prediction error. These results suggest that several functional subgroups of dorsal striatal neurons represent different kinds of information formed through extensive prior exposure to CS-reward contingencies.

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Reward Prediction Error Coding in Dorsal Striatal Neurons

Cited by 84 publications

References 50 publications

The Role of Striatal Tonically Active Neurons in Reward Prediction Error Signaling during Instrumental Task Performance

The Role of Striatal Tonically Active Neurons in Reward Prediction Error Signaling during Instrumental Task Performance

The role of learning-related dopamine signals in addiction vulnerability

Discrete coding of stimulus value, reward expectation, and reward prediction error in the dorsal striatum

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