Erratum: Arithmetic and local circuitry underlying dopamine prediction errors

Eshel, Neir; Bukwich, Michael; Rao, Vinod; Hemmelder, Vivian; Tian, Ju; Uchida, Naoshige

doi:10.1038/nature15718

Cited by 5 publications

(2 citation statements)

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“…The VTA contains at least two electrophysiologically and pharmacologically distinct types of local GABAergic neurons ( 17 , 18 ). The GABAergic identity of T2 and T3Ns was directly examined in opto-tagging experiments of Cohen et al ( 1 ) and Eshel et al ( 19 ). In these studies, optogenetic transgene expression was found in ~1/3 ( 1 ) and ~1/2 ( 19 ) of the TH−/VGAT+ and TH−/GAD67+ neurons, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The GABAergic identity of T2 and T3Ns was directly examined in opto-tagging experiments of Cohen et al ( 1 ) and Eshel et al ( 19 ). In these studies, optogenetic transgene expression was found in ~1/3 ( 1 ) and ~1/2 ( 19 ) of the TH−/VGAT+ and TH−/GAD67+ neurons, respectively. Cohen et al ( 1 ) found, using ChR2 targeting, that all physiologically defined T2-cluster neurons corresponded to light-responsive and all T3-cluster cells to the unresponsive neurons.…”

Section: Resultsmentioning

confidence: 99%

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

Stetsenko,

Koos

2023

Proc. Natl. Acad. Sci. U.S.A.

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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.

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