Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making
Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making
复制标题
纹状体通路竞争的奖励驱动变化塑造决策中的证据评估
DOI：
10.1371/journal.pcbi.1006998
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发表时间：
2019-05-01
影响因子：
4.3
通讯作者：
Rubin, Jonathan
中科院分区：
文献类型：
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作者：
Dunovan, Kyle;Vich, Catalina;Rubin, Jonathan
皮质-基底神经节-丘脑（CBGT）网络对于自适应决策至关重要，但电路级特性的变化如何影响认知算法仍不清楚。在这里，我们探讨如何多巴胺可塑性皮质纹状体突触改变纹状体通路之间的竞争，影响决策过程中的证据积累过程。尖峰定时依赖的可塑性模拟表明，多巴胺能反馈的基础上的奖励修改的比例，直接和间接的皮质纹状体的重量内反对的行动渠道。在一个完整的尖峰CBGT网络模型中使用学习的权重比，我们模拟了奖励驱动的决策任务中的神经动力学和决策结果，并将其与漂移扩散模型相拟合。拟合显示，证据积累的速率随直接途径活性的通道间差异而变化，而边界高度随总体间接途径活性而变化。这种多层次的建模方法演示了如何互补的学习和决策计算可以出现从corticostriatal plasticity.Author摘要认知过程模型，如强化学习（RL）和漂移扩散模型（DDM），有助于阐明基本算法错误纠正学习和评估积累决策证据导致的选择。虽然这些相对抽象的模型有助于指导相关现象的实验和理论探索，但它们仍然无法提供关于在适应性选择行为期间在神经生物学基底中进行学习和决策算法的实际物理机制的信息。在这里，我们提出了一个向上映射的方法来桥接基于价值的决策的神经和认知模型，显示多巴胺能反馈如何改变学习过程中的皮质基底神经节丘脑（CBGT）通路的网络水平的动态偏向行为选择更有益的行动。通过映射分析的水平，这种方法产生了关于神经元活动方面的具体预测，这些预测映射到认知决策框架中出现的量。
Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity.Author summary Cognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an upwards mapping approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping up the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.