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首页> 外文期刊>Frontiers in Neural Circuits >Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity
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Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity

机译：具有奖励依赖可塑性的尖峰模型中研究的不同基底神经节通路的功能相关性

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The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we have developed a spiking model of the basal ganglia (BG) that learns to dis-inhibit the action leading to a reward despite ongoing changes in the reward schedule. The architecture of the network features the two pathways commonly described in BG, the direct (denoted D1) and the indirect (denoted D2) pathway, as well as a loop involving striatum and the dopaminergic system. The activity of these dopaminergic neurons conveys the reward prediction error (RPE), which determines the magnitude of synaptic plasticity within the different pathways. All plastic connections implement a versatile four-factor learning rule derived from Bayesian inference that depends upon pre- and post-synaptic activity, receptor type, and dopamine level. Synaptic weight updates occur in the D1 or D2 pathways depending on the sign of the RPE, and an efference copy informs upstream nuclei about the action selected. We demonstrate successful performance of the system in a multiple-choice learning task with a transiently changing reward schedule. We simulate lesioning of the various pathways and show that a condition without the D2 pathway fares worse than one without D1. Additionally, we simulate the degeneration observed in Parkinson's disease (PD) by decreasing the number of dopaminergic neurons during learning. The results suggest that the D1 pathway impairment in PD might have been overlooked. Furthermore, an analysis of the alterations in the synaptic weights shows that using the absolute reward value instead of the RPE leads to a larger change in D1.
机译：大脑能够使动物适应行为，以便在复杂而动态的环境中生存，但是如何通过其底层的神经回路实现和计算奖励导向的行为却是一个悬而未决的问题。为了解决这一问题，我们开发了基底神经节（BG）的突跳模型，该模型学会了在奖励时间表不断变化的情况下抑制产生奖励的行为。该网络的体系结构具有BG中通常描述的两个路径，即直接（表示为D1）和间接（表示为D2）路径，以及涉及纹状体和多巴胺能系统的回路。这些多巴胺能神经元的活动传达了奖励预测误差（RPE），该误差决定了不同途径中突触可塑性的大小。所有塑料连接都实现了一种通用的四因素学习规则，该规则是根据贝叶斯推断得出的，该规则取决于突触前后的活动，受体类型和多巴胺水平。取决于RPE的征兆，D1或D2途径中会发生突触权重更新，而有效拷贝会告知上游核有关所选作用的信息。我们证明了系统在多项选择的学习任务中的成功表现，并且奖励时间表发生了变化。我们模拟了各种途径的损害，并表明没有D2途径的病情要比没有D1的病情更糟。此外，我们通过减少学习过程中多巴胺能神经元的数量来模拟在帕金森氏病（PD）中观察到的变性。结果表明PD中的D1通路损伤可能已被忽略。此外，对突触权重变化的分析表明，使用绝对奖励值代替RPE会导致D1的较大变化。

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