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Dopaminergic Control of the Exploration-Exploitation Trade-Off via the Basal Ganglia

机译:基底神经节对勘探开发权衡的多巴胺能控制

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摘要

We continuously face the dilemma of choosing between actions that gather new information or actions that exploit existing knowledge. This “exploration-exploitation” trade-off depends on the environment: stability favors exploiting knowledge to maximize gains; volatility favors exploring new options and discovering new outcomes. Here we set out to reconcile recent evidence for dopamine’s involvement in the exploration-exploitation trade-off with the existing evidence for basal ganglia control of action selection, by testing the hypothesis that tonic dopamine in the striatum, the basal ganglia’s input nucleus, sets the current exploration-exploitation trade-off. We first advance the idea of interpreting the basal ganglia output as a probability distribution function for action selection. Using computational models of the full basal ganglia circuit, we showed that, under this interpretation, the actions of dopamine within the striatum change the basal ganglia’s output to favor the level of exploration or exploitation encoded in the probability distribution. We also found that our models predict striatal dopamine controls the exploration-exploitation trade-off if we instead read-out the probability distribution from the target nuclei of the basal ganglia, where their inhibitory input shapes the cortical input to these nuclei. Finally, by integrating the basal ganglia within a reinforcement learning model, we showed how dopamine’s effect on the exploration-exploitation trade-off could be measurable in a forced two-choice task. These simulations also showed how tonic dopamine can appear to affect learning while only directly altering the trade-off. Thus, our models support the hypothesis that changes in tonic dopamine within the striatum can alter the exploration-exploitation trade-off by modulating the output of the basal ganglia.
机译:我们一直面临着在选择收集新信息的操作或利用现有知识的操作之间进行选择的困境。这种“探索-开发”的取舍取决于环境:稳定有利于利用知识来最大化收益;波动性有利于探索新的选择并发现新的结果。在这里,我们通过检验以下假设,调和纹状体中的多巴胺(基础神经节的输入核)设定多巴胺的假设,以调和近期有关多巴胺参与勘探开发权衡的证据与基础神经节控制行动选择的现有证据。当前的勘探与开发权衡。我们首先提出将基底神经节输出解释为动作选择的概率分布函数的想法。使用整个基底神经节回路的计算模型,我们表明,在这种解释下,纹状体内多巴胺的作用改变了基底神经节的输出,从而有利于概率分布中编码的勘探或开发水平。我们还发现,如果我们改为从基底神经节的目标核中读出概率分布,则其模型预测纹状体多巴胺将控制勘探与开发之间的权衡,在那里它们的抑制性输入决定了这些核的皮质输入。最后,通过将基础神经节整合到强化学习模型中,我们展示了在强制两选任务中如何测量多巴胺对勘探与开发权衡的影响。这些模拟还显示了补品多巴胺如何在仅直接改变权衡的情况下似乎会影响学习。因此,我们的模型支持这样的假设,即纹状体内的多巴胺滋补剂的变化可以通过调节基底神经节的输出来改变勘探与开发的权衡。

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