Dopamine Operates as a Subsecond Modulator of Food Seeking

Roitman, Mitchell F.; Stuber, Garret D.; Phillips, Paul E. M.; Wightman, R. Mark; Carelli, Regina M.

doi:10.1523/jneurosci.3823-03.2004

Cited by 610 publications

(598 citation statements)

References 40 publications

(53 reference statements)

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“…With respect to (b), although there are no direct methods for measuring intra-synaptic dopamine levels with subsecond resolution (Robinson et al, 2003), fast-scan cyclic voltammetry studies provide strong evidence that both direct electrical stimulation of dopamine neurons and behaviorally relevant events (eg natural rewards) lead to phasic overflow of dopamine from the synapse (Garris et al, 1997;Phillips et al, 2003;Rebec et al, 1997;Roitman et al, 2004). However, it is worth noting that dopamine neuron firing and dopamine release may in fact be doubly dissociable (Garris et al, 1999;Grace, 1991).…”

Section: Discussion Limitationsmentioning

confidence: 99%

Linking Animal Models of Psychosis to Computational Models of Dopamine Function

Smith

Becker

et al. 2006

Neuropsychopharmacol

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Psychosis is linked to dysregulation of the neuromodulator dopamine and antipsychotic drugs (APDs) work by blocking dopamine receptors. Dopamine-modulated disruption of latent inhibition (LI) and conditioned avoidance response (CAR) have served as standard animal models of psychosis and antipsychotic action, respectively. Meanwhile, the 'temporal difference' algorithm (TD) has emerged as the leading computational model of dopamine neuron firing. In this report TD is extended to include action at the level of dopamine receptors in order to explain a number of behavioral phenomena including the dose-dependent disruption of CAR by APDs, the temporal dissociation of the effects of APDs on receptors vs behavior, the facilitation of LI by APDs, and the disruption of LI by amphetamine. The model also predicts an APD-induced change to the latency profile of CARFa novel prediction that is verified experimentally. The model's primary contribution is to link dopamine neuron firing, receptor manipulation, and behavior within a common formal framework that may offer insights into clinical observations. Neuropsychopharmacology Keywords: dopamine; temporal difference learning; psychosis; conditioned avoidance; latent inhibition; computational modeling INTRODUCTIONPerturbations in dopamine transmission are central to a number of human illnesses including addiction, Parkinson's disease, attention-deficit hyperactivity disorder (ADHD), and schizophrenia. As a result there has been tremendous interest in understanding the psychological and behavioral functions of dopamine, and a number of overlapping hypotheses have been suggested. These hypotheses include roles for dopamine in hedonia (Wise, 1982), reward learning (Robbins and Everitt, 1996), incentive salience (Berridge and Robinson, 1998), sensorimotor function and anergia (Salamone et al, 1994). In parallel, mathematical models have been used to link some of these psychological constructs to the physiology of the dopamine system (Schultz, 1998;Seamans and Yang, 2004). Many of these models have concentrated on 'normal' dopaminergic conditions (McClure et al, 2003;Schultz et al, 1997), and the current aim is to explore how one such model can be extended to address the abnormal conditions encountered in schizophrenia and their treatment.Of all the mathematical models linking dopamine, behavior, and psychology, the Temporal Difference Learning model (TD) has enjoyed particular success (Montague et al, 1996;Redish, 2004;Schultz et al, 1997). TD is a powerful formal reinforcement learning technique that has been used to solve many challenging machine learning problems. For example, the technique has been used to train computers to play backgammon to the highest human standards by associating a simulated reward with winning a game (Tesauro, 1994). At the heart of TD is a predictionerror signal that is zero if the environment behaves as expected, positive in response to unexpected reward, and negative following omission of expected reward. Striking parallels have been observed between...

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Section: Discussion Limitationsmentioning

confidence: 99%

Linking Animal Models of Psychosis to Computational Models of Dopamine Function

Smith

Becker

et al. 2006

Neuropsychopharmacol

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“…Electrophysiological studies have shown that dopaminergic neurons fire not only in response to unexpected reward but also to cues that predict delivery of reward (Schultz et al, 1997). Moreover, Roitman et al (2004) showed that reward-predicting cues cause midbrain dopamine projections to release dopamine in regions of the ventral striatum, including the NAc. Similarly, dopamine measurements in the NAc using fast-scan cyclic voltammetry have shown a greater amount of dopamine delivered following an S+ compared to an S− cue after Pavlovian conditioning (Day et al, 2007;Stuber et al, 2008).…”

Section: Physiological Origin Of the O 2 Signalmentioning

confidence: 99%

Changes in reward-related signals in the rat nucleus accumbens measured by in vivo oxygen amperometry are consistent with fMRI BOLD responses in man

et al. 2012

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a b s t r a c t a r t i c l e i n f oReal-time in vivo oxygen amperometry, a technique that allows measurement of regional brain tissue oxygen (O 2 ) has been previously shown to bear relationship to the BOLD signal measured with functional magnetic resonance imaging (fMRI) protocols. In the present study, O 2 amperometry was applied to the study of reward processing in the rat nucleus accumbens to validate the technique with a behavioural process known to cause robust signals in human neuroimaging studies. After acquisition of a cued-lever pressing task a robust increase in O 2 tissue levels was observed in the nucleus accumbens specifically following a correct lever press to the rewarded cue. This O 2 signal was modulated by cue reversal but not lever reversal, by differences in reward magnitudes and by the motivational state of the animal consistent with previous reports of the role of the nucleus accumbens in both the anticipation and representation of reward value. Moreover, this modulation by reward value was related more to the expected incentive value rather than the hedonic value of reward, also consistent with previous reports of accumbens coding of "wanting" of reward. Altogether, these results show striking similarities to those obtained in human fMRI studies suggesting the use of oxygen amperometry as a valid surrogate for fMRI in animals performing cognitive tasks, and a powerful approach to bridge between different techniques of measurement of brain function.

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“…How is this phasic change in firing activity translated into DA release at target structures? Experimental data has shown that a single-pulse stimulation applied to the VTA or the median forebrain bundle leads to a DA release lasting a few seconds (Kilpatrick et al 2000;Fields et al 2007;Phillips et al 2003;Robinson et al 2002;Roitman et al 2004;Yavich and MacDonald 2000). The probability of release of DA is however lower than that of glutamate and is varies greatly among DA synapses (Daniel et al 2009).…”

Section: (T)mentioning

confidence: 99%

Computational models of reinforcement learning: the role of dopamine as a reward signal

2010

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Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success.

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Dopamine Operates as a Subsecond Modulator of Food Seeking

Cited by 610 publications

References 40 publications

Linking Animal Models of Psychosis to Computational Models of Dopamine Function

Linking Animal Models of Psychosis to Computational Models of Dopamine Function

Changes in reward-related signals in the rat nucleus accumbens measured by in vivo oxygen amperometry are consistent with fMRI BOLD responses in man

Computational models of reinforcement learning: the role of dopamine as a reward signal

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