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首页> 美国卫生研究院文献>Frontiers in Neural Circuits >Minimal Circuit Model of Reward Prediction Error Computations and Effects of Nicotinic Modulations
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Minimal Circuit Model of Reward Prediction Error Computations and Effects of Nicotinic Modulations

机译：奖励预测误差计算的最小电路模型和烟碱调节作用

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Dopamine (DA) neurons in the ventral tegmental area (VTA) are thought to encode reward prediction errors (RPE) by comparing actual and expected rewards. In recent years, much work has been done to identify how the brain uses and computes this signal. While several lines of evidence suggest the interplay of the DA and the inhibitory interneurons in the VTA implements the RPE computation, it still remains unclear how the DA neurons learn key quantities, for example the amplitude and the timing of primary rewards during conditioning tasks. Furthermore, endogenous acetylcholine and exogenous nicotine, also likely affect these computations by acting on both VTA DA and GABA (γ -aminobutyric acid) neurons via nicotinic-acetylcholine receptors (nAChRs). To explore the potential circuit-level mechanisms for RPE computations during classical-conditioning tasks, we developed a minimal computational model of the VTA circuitry. The model was designed to account for several reward-related properties of VTA afferents and recent findings on VTA GABA neuron dynamics during conditioning. With our minimal model, we showed that the RPE can be learned by a two-speed process computing reward timing and magnitude. By including models of nAChR-mediated currents in the VTA DA-GABA circuit, we showed that nicotine should reduce the acetylcholine action on the VTA GABA neurons by receptor desensitization and potentially boost DA responses to reward-related signals in a non-trivial manner. Together, our results delineate the mechanisms by which RPE are computed in the brain, and suggest a hypothesis on nicotine-mediated effects on reward-related perception and decision-making.
机译：腹侧被盖区（VTA）中的多巴胺（DA）神经元被认为可以通过比较实际和预期的奖励来编码奖励预测错误（RPE）。近年来，已经做了很多工作来识别大脑如何使用和计算该信号。虽然有几条证据表明DA和VTA中抑制性神经元之间的相互作用实现了RPE计算，但仍不清楚DA神经元如何学习关键量，例如在调节任务期间主要奖励的幅度和时机。此外，内源性乙酰胆碱和外源尼古丁也可能通过烟碱型乙酰胆碱受体（nAChRs）作用于VTA DA和GABA（γ-氨基丁酸）神经元，从而影响这些计算。为了探索经典调节任务期间RPE计算的潜在电路级机制，我们开发了VTA电路的最小计算模型。该模型旨在解决VTA传入者与奖励相关的一些属性以及调节期间VTA GABA神经元动力学的最新发现。通过我们的最小模型，我们表明可以通过两步过程计算奖励时间和幅度来学习RPE。通过在VTA DA-GABA电路中包含nAChR介导的电流模型，我们表明尼古丁应通过受体脱敏作用减少对VTA GABA神经元的乙酰胆碱作用，并可能以非平凡的方式增强DA对奖励相关信号的反应。总之，我们的结果描述了在大脑中计算RPE的机制，并提出了关于尼古丁介导的与奖励相关的感知和决策的影响的假设。

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